CN114341366A - Biomarkers and methods for personalized treatment of small cell lung cancer using KDM1A inhibitors - Google Patents

Abstract

The present application discloses biomarkers and methods for predicting responsiveness of patients with small cell lung cancer (SCLC) to treatment with KDM1A inhibitors, as well as methods of treating subgroups of SCLC patients identified using the methods.

Description

Biomarkers and Methods for Personalized Treatment of Small Cell Lung Cancer Using KDM1A Inhibitors

technical field

The present invention relates to biomarkers and methods for personalized treatment of small cell lung cancer (SCLC) using KDM1A inhibitors. The present invention provides methods of identifying SCLC patients who may benefit from treatment with a KDM1A inhibitor and methods of treating such patients with a KDM1A inhibitor.

Background technique

Lysine-specific demethylase 1 (LSD1, also known as KDM1A) is a histone-modifying enzyme responsible for the demethylation of dimethyl histone 3 lysine 4 (H3K4me2) (Shi et al. , Cell 2004). In several human cancers, KDM1A overexpression has been associated with disease progression and poor prognosis, and its inhibition has been shown to reduce cancer cell growth, migration and invasion. As such, KDM1A has been considered a target of interest for the development of new drugs to treat cancer, and several KDM1A inhibitors are currently in clinical trials in oncology.

In particular, KDM1A inhibitors have been reported to be effective in the treatment of small cell lung cancer (SCLC). KDM1A inhibition has been shown to reduce proliferation of SCLC cell lines in vitro and retard tumor growth in SCLC-bearing xenograft mice (Mohammad et al., 2015, Cancer Cell 28, 57-69). However, published data suggest that while some SCLCs are highly sensitive to KDM1A inhibition, sensitivity to KDM1A inhibition is not a universal feature of SCLC cells. This necessitates the development of approaches to individualize SCLC treatment with KDM1A inhibitors, particularly patient selection methods to identify those SCLC patients who would benefit or would benefit most from receiving KDM1A inhibitor therapy.

Therefore, the technical problem of the present invention is to provide tools and methods for identifying and treating SCLC patients most suitable for treatment with KDM1A inhibitors.

SUMMARY OF THE INVENTION

The present invention provides tools and methods for individualized treatment of small cell lung cancer (SCLC) using KDM1A inhibitors.

In one aspect, the invention provides a method of identifying SCLC patients who are more likely to respond to treatment comprising a KDM1A inhibitor, the method comprising measuring ASCL1 and levels of SOX2. In some embodiments, a patient is identified as more likely to respond to treatment comprising a KDM1A inhibitor when the levels of each of ASCL1 and SOX2 in the sample exceed a threshold.

For example, patients were identified as more likely to respond to treatment comprising a KDM1A inhibitor, compared to patients with SCLC and had measured ASCL1 and SOX2 levels from patient samples prior to initiation of treatment comprising a KDM1A inhibitor (when When the respective levels of ASCL1 and SOX2 in the sample do not exceed the threshold). Conversely, the latter patients (where the levels of ASCL1 and SOX2 in the samples each did not exceed thresholds) were less likely to respond to treatment comprising a KDM1A inhibitor. This exemplary explanation applies to all aspects and uses provided herein relating to, encompassing or including identifying SCLC patients who are more likely to respond/respond to treatments comprising KDM1A inhibitors and the like.

In some embodiments, the method further comprises using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, wherein when the score in the sample exceeds a threshold, the patient is identified as being more likely to respond to a KDM1A inhibitor-containing treatment response.

In another aspect, the invention provides a method of identifying SCLC patients who may benefit from treatment comprising a KDM1A inhibitor, the method comprising measuring ASCL1 and SOX2 in a sample from the patient prior to initiating treatment comprising a KDM1A inhibitor s level. In some embodiments, a patient is identified as a patient who may benefit from treatment comprising a KDM1A inhibitor when the level of each of ASCL1 and SOX2 in the sample exceeds a threshold. In some embodiments, the method further comprises using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, wherein when the score in the sample exceeds a threshold, the patient is identified as being able to benefit from treatment comprising a KDM1A inhibitor of patients.

In a further aspect, the invention provides a method of predicting the responsiveness of a SCLC patient to a treatment comprising a KDM1A inhibitor, the method comprising measuring ASCL1 and SOX2 in a sample from the patient prior to initiating treatment comprising a KDM1A inhibitor s level. In some embodiments, a patient is identified as being more likely to respond to treatment comprising a KDM1A inhibitor when the level of each of ASCL1 and SOX2 in the sample exceeds a threshold. In some embodiments, the method further comprises using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, wherein when the score in the sample exceeds a threshold, the patient is identified as being more likely to respond to a KDM1A inhibitor-containing Treatment responds.

In a further aspect, the invention provides a method of assessing the likelihood of a SCLC patient's response to therapy comprising a KDM1A inhibitor, the method comprising measuring ASCL1 and SOX2 in a sample from the patient prior to initiation of therapy comprising a KDM1A inhibitor s level. In some embodiments, a patient is identified as more likely to respond to treatment comprising a KDM1A inhibitor when the levels of each of ASCL1 and SOX2 in the sample exceed a threshold. In some embodiments, the method further comprises using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, wherein when the score in the sample exceeds a threshold, the patient is identified as being more likely to respond to a KDM1A inhibitor-containing treatment response.

In a further aspect, the present invention provides a method of assessing the likelihood of a SCLC patient's response to therapy comprising a KDM1A inhibitor, the method comprising measuring ASCL1 and ASCL1 and levels of SOX2. In some embodiments, SCLCs are identified as more likely to respond to treatment comprising a KDM1A inhibitor when the levels of each of ASCL1 and SOX2 in the sample exceed a threshold. In some embodiments, the method further comprises using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, wherein when the score in the sample exceeds a threshold, SCLCs are identified as more likely to be sensitive to a KDM1A inhibitor-containing treatment response.

In a further aspect, the present invention provides a method of selecting therapy for a patient with SCLC, the method comprising measuring the levels of ASCL1 and SOX2 in a sample from the patient prior to initiating therapy. In some embodiments, when the level of each of ASCL1 and SOX2 in the sample exceeds a threshold, the method includes providing a recommendation that the treatment selected for the patient comprises a KDM1A inhibitor. In some embodiments, the method further comprises using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, and when the score in the sample exceeds a threshold, providing a recommendation that the treatment selected for the patient comprises a KDM1A inhibitor. In other words, for example, when the level of each of ASCL1 and SOX2 in the sample exceeds a threshold, or when the score in the sample exceeds a threshold, the treatment selected for a patient with SCLC is a treatment comprising a KDM1A inhibitor. When the respective levels of ASCL1 and SOX2 do not exceed the threshold, or when the score does not exceed the threshold, other treatment options other than KDM1A inhibitor-containing therapy may be considered for the patient, such as the use of drugs/treatments other than KDM1A inhibitors drug treatment.

In a further aspect, the present invention provides a method of treating a patient with SCLC, if the patient has been identified as being more likely to respond to a treatment comprising a KDM1A inhibitor using a method according to any of the preceding aspects prior to initiation of therapy comprising a KDM1A inhibitor response to treatment, the method comprises administering to the patient a therapeutically effective amount of a treatment comprising a KDM1A inhibitor.

In a further aspect, the invention features a method of treating a patient with SCLC, the method comprising measuring the levels of ASCL1 and SOX2 in a sample from the patient prior to initiating treatment, when the levels of each of ASCL1 and SOX2 in the sample exceed a threshold value , the patient is identified as being more likely to respond to a treatment comprising a KDM1A inhibitor, and if the patient is identified as being more likely to respond, a therapeutically effective amount of a treatment comprising a KDM1A inhibitor is administered to the patient.

In a further aspect, the invention features a method of treating a patient with SCLC, the method comprising measuring the levels of ASCL1 and SOX2 in a sample from the patient prior to initiation of treatment, using these levels to generate a score for the sample, when the sample is A patient is identified as more likely to respond to a treatment comprising a KDM1A inhibitor when the score in <RTI ID=0.0>in </RTI> exceeds a threshold, and if the patient is identified as more likely to respond, the patient is administered a therapeutically effective amount of a treatment comprising a KDM1A inhibitor.

In a further aspect, the present invention provides a KDM1A inhibitor for use in the treatment of a patient with SCLC, wherein the patient has been identified as being more likely to have KDM1A-containing prior to initiation of therapy comprising the KDM1A inhibitor, using a method according to any of the preceding aspects Therapeutic response to inhibitors.

In a further aspect, the invention provides the use of ASCL1 and SOX2 in a method of identifying SCLC patients who are more likely to respond to treatment comprising a KDM1A inhibitor.

In a further aspect, the present invention provides the use of ASCL1 and SOX2 in a method of assessing the likelihood of a SCLC patient's response to therapy comprising a KDM1A inhibitor.

In a further aspect, the invention provides the use of one or more reagents for measuring levels of ASCL1 and SOX2 in a method of identifying SCLC patients who are more likely to respond to treatment comprising a KDM1A inhibitor.

In a further aspect, the present invention provides the use of one or more reagents for measuring the levels of ASCL1 and SOX2 in a method of assessing the likelihood of a SCLC patient's response to therapy comprising a KDM1A inhibitor.

In a further aspect, the present invention provides the use of ASCL1 and SOX2 in the manufacture of a diagnostic for identifying SCLC patients more likely to respond to treatment comprising a KDM1A inhibitor.

In a further aspect, the present invention provides the use of ASCL1 and SOX2 in the manufacture of a diagnostic agent for assessing the likelihood of a SCLC patient's response to therapy comprising a KDM1A inhibitor.

In another aspect, the invention provides a kit for identifying SCLC patients more likely to respond to treatment comprising a KDM1A inhibitor, the kit comprising one or more for measuring ASCL1 and SOX2 in a sample level of reagents, and optionally, instructions for use.

In another aspect, the invention provides a kit for assessing the likelihood of a SCLC patient's response to a treatment comprising a KDM1A inhibitor, the kit comprising one or more for measuring the levels of ASCL1 and SOX2 in a sample The reagents and, optionally, instructions for use.

Description of drawings

Figure 1: Dot plot represents ASCL1 (Y-axis) and SOX2 (X-axis) of SCLC cell lines sensitive (grey dots), partially sensitive (open squares) or resistant (black triangles) to KDM1A inhibition as measured by RNA-seq expression (Log2(RPKM) value), as described in more detail in Example 2.

Figure 2: Dot plot represents gene expression (absolute Cp values) of ASCL1 and SOX2 in SCLC cell lines sensitive (grey dots), partially sensitive (open squares) or resistant (black diamonds) to KDM1A inhibition as measured by qRT-PCR , as described in Example 3. The values plotted are the mean of independent experiments. Values in parentheses have Cp values for SOX2 expression above 40.

Figure 3: Dot plot represents gene expression of ASCL1 and SOX2 in an expanded panel of SCLC cell lines sensitive (grey dots), partially sensitive (open squares) or resistant (black triangles) to KDM1A inhibition as measured by microarray Affymetrix analysis (RMA value), as described in Example 4.

4 : ROC curves based on gene expression of ASCL1 ( FIG. 4A ) and SOX2 ( FIG. 4B ) for distinguishing KDM1Ai-sensitive and resistant SCLC cells, as described in Example 4. FIG. Sensitivity and specificity for a given threshold and their respective confidence intervals are shown in the table below each graph.

Figure 5: Western blot (WB) (Figure 5A) and quantification (Figure 5B) of ASCL1 and SOX2 protein levels in NCI-H146, NCI-H510A, NCI-H446 and NCI-H526 cell lines, as described in Example 5.1 .

Figure 6: Correlation between protein and mRNA (CCLE Affymetrix) levels of SOX2 (Figure 6A) and ASCL1 (Figure 6B), as described in Example 5.1.

Figure 7: Fluorescent immunohistochemical staining of SOX2 (Figure 7A), ASCL1 (Figure 7B) and negative control (no primary antibody, Figure 7C) according to Example 5.2 showing low levels of both biomarkers in NCI-H526 cells /undetectable levels, low/undetectable levels of ASCL1 in NCI-H446 cells, and the highest levels of both biomarkers in NCI-H146 cells, consistent with their mRNA levels. DAPI: DAPI (4',6-diamidino-2-phenylindole) is a fluorescent dye that binds strongly to AT-rich regions of DNA and stains the nucleus. No signal was detected in the negative control containing only the secondary antibody (AF546: AlexaFluor 546).

Figure 8: Correlation between protein and mRNA (CCLE Affymetrix) levels of SOX2 (Figure 8A) and ASCL1 (Figure 8B), as described in more detail in Example 5.2.

Figure 9: Representative ASCL1, SOX2 and their corresponding DAPI fluorescent immunohistochemical images from SCLC PDX TMA for each staining classification level 0, 1, 2 and 3 are shown, as described in Example 5.3.

Figure 10: Two-tailed Spearman correlation test between RNASeq (Log ₂ FPKM) and IF (visual score) for SOX2 (Figures 10A and 10B) and ASCL1 (Figures 10C and 10D) shown for two independent experiments, confidence The interval is 95% (coefficients are specified at the bottom of each figure), as described in Example 5.3.

Figure 11: WB and Ponceaustaining of ASCL1 and SOX2 in exosome fractions according to Example 6. Consistent with mRNA expression, ASCL1 was not detected in exosomes derived from NCI-H446 and NCI-H526, and SOX2 was absent in exosomes derived from NCI-H526.

Figure 12: WB (left) and Ponceau staining (right) of ASCL1, SOX2 and CD151 (lung cancer specific exosomal markers) in exosomes and corresponding parental cells according to Example 6. After treatment of NCI-H510A cells with 5 μM GW4869 (an inhibitor of exosome production) for 48 hours, the ASCL1, SOX2 and CD151 signals in the exosome fraction were significantly reduced or disappeared, while the expression of these proteins in the cells was unaffected, showed that the detection of ASCL1, SOX2 and CD151 was exosome-specific.

Detailed ways

definition

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below.

All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety.

Unless otherwise stated, the nomenclature used in this application is based on the IUPAC systematic nomenclature.

Unless otherwise stated, any open valence present on a carbon, oxygen, sulfur or nitrogen atom in the structures herein indicates the presence of hydrogen.

Stereochemical definitions and conventions used herein generally follow S.P. Parker, ed., McGraw-Hill Dictionary of Chemical Terms (1984) McGraw-Hill Book Company, New York; and Eliel, E. and Wilen, S., "Stereochemistry of Organic Compounds" , John Wiley & Sons, Inc., New York, 1994. When describing optically active compounds, the prefixes D and L, or R and S, are used to denote the absolute configuration of the molecule about its chiral center. The substituents attached to the chiral centers under consideration are arranged according to the order of Cahn, Ingold and Prelog. (Cahn et al. Angew. Chem. Inter. ed. 1966, 5, 385; errata 511). The prefixes D and L or (+) and (-) are employed to denote the sign of rotation of the compound for plane polarized light, where (-) or L indicates that the compound is levorotatory. Compounds with the prefix (+) or D are dextrorotatory.

The terms "optional" or "optionally" mean that the subsequently described event or circumstance can, but need not, occur, and that the description includes instances where the event or circumstance occurs and instances in which it does not.

The term "pharmaceutically acceptable salt" means a salt that is not biologically or otherwise undesirable. Pharmaceutically acceptable salts include acid addition salts and base addition salts. Pharmaceutically acceptable salts are well known in the art.

The term "pharmaceutically acceptable acid addition salts" refers to those pharmaceutically acceptable salts formed with inorganic and organic acids, such as hydrochloric, hydrobromic, sulfuric, nitric, carbonic, phosphoric, The organic acid is selected from the group consisting of aliphatic, cycloaliphatic, aromatic, araliphatic, heterocyclic, carboxylic and sulfonic acids, such as formic acid, acetic acid, propionic acid, glycolic acid, gluconic acid, lactic acid, pyruvic acid, Oxalic acid, malic acid, maleic acid, malonic acid, succinic acid, fumaric acid, tartaric acid, citric acid, aspartic acid, ascorbic acid, glutamic acid, anthranilic acid, benzoic acid, cinnamic acid, mandelic acid, Pamoic acid, phenylacetic acid, methanesulfonic acid, ethanesulfonic acid, p-toluenesulfonic acid and salicylic acid.

The term "pharmaceutically acceptable base addition salts" refers to those pharmaceutically acceptable salts formed with organic or inorganic bases. Examples of acceptable inorganic bases include sodium, potassium, ammonium, calcium, magnesium, iron, zinc, copper, manganese, and aluminum salts. Salts derived from pharmaceutically acceptable organic non-toxic bases include salts of primary, secondary and tertiary amines, substituted amines (including naturally occurring substituted amines), cyclic amines and basic ion exchange resins such as isopropylamine, Trimethylamine, diethylamine, triethylamine, tripropylamine, ethanolamine, 2-diethylaminoethanol, trimethylamine, dicyclohexylamine, lysine, arginine, histidine, caffeine, proca In, hebamine, choline, betaine, ethylenediamine, glucosamine, methylglucamine, theobromine, purine, piperazine, piperidine, N-ethylpiperidine and polyamine resins.

The terms "KDM1A inhibitor" or "KDM1Ai" are used interchangeably and, as used herein, refer to any compound capable of inhibiting the activity of KDM1A. Methods for determining KDM1A inhibitory activity are well known in the art. In preferred embodiments, the KDM1A inhibitor is a small molecule. Examples of KDM1A inhibitors are described in more detail elsewhere herein.

As used herein, "small molecule" refers to an organic compound having a molecular weight below 900 Daltons, preferably below 500 Daltons. Molecular weight is the mass of a molecule calculated as the sum of the atomic weights of each constituent element times the number of atoms of that element in the molecular formula.

"Treatment comprising a KDM1A inhibitor" refers to any therapy or treatment regimen that incorporates a KDM1A inhibitor, whether as the sole active pharmaceutical ingredient (API) or in combination with one or more other APIs (eg, other anticancer agent) combination. The treatment comprising a KDM1A inhibitor is usually in the form of a pharmaceutical composition. If a treatment comprising a KDM1A inhibitor comprises one or more APIs in addition to the KDM1A inhibitor, it can be administered in a single pharmaceutical composition incorporating all APIs, or it can be administered with each API (ie, KDM1A inhibitor and One or more other APIs) are administered as separate pharmaceutical compositions, which may be administered by the same or different routes (eg, one may be administered orally and the other parenterally), and may be administered simultaneously or sequentially.

The terms "pharmaceutical composition" and "pharmaceutical formulation" are used interchangeably and refer to a therapeutically effective amount of an active pharmaceutical ingredient (eg, a KDM1A inhibitor) and one or more of the ingredients to be administered to a mammal, such as a human in need thereof Compositions (eg, mixtures or solutions) of pharmaceutically acceptable excipients.

The term "pharmaceutically acceptable" refers to the properties of substances that can be used in the preparation of pharmaceutical compositions that are generally safe, non-toxic, biologically or otherwise undesirable, and are suitable for veterinary and Human medicinal use is acceptable.

The terms "pharmaceutically acceptable excipient", "pharmaceutically acceptable carrier" and "therapeutically inert excipient" are used interchangeably and refer to any pharmaceutically acceptable ingredient in a pharmaceutical composition that does not Has therapeutic activity and is nontoxic to the subject to which it is administered, such as disintegrants, binders, fillers, solvents, buffers, tonicity agents, stabilizers, antioxidants, surfactants, carrier, diluent or lubricant.

The term "therapeutically effective amount" (or "effective amount") refers to the amount of a compound of the present invention which, when administered to a patient, (i) treats or prevents a particular disease, (ii) attenuates, ameliorates or eliminates one or more of the disease symptoms, or (iii) preventing or delaying the onset of one or more symptoms of the disease. The therapeutically effective amount will vary depending on the compound, the disease state being treated, the severity of the disease being treated, the age and relative health of the patient, the route and form of administration, the judgment of the attending physician or veterinary practitioner, and other factors.

The term "treating" of a disease (eg SCLC) and its various grammatical forms includes reversing, alleviating or inhibiting the progression of the disease or one or more symptoms thereof.

"Patient" or "subject" are used interchangeably and refer to a mammal in need of treatment. Mammals include, but are not limited to, primates (eg, humans and non-human primates, eg, monkeys), domesticated animals (eg, cows, sheep, cats, dogs, and horses), and laboratory animals (mice, rats, guinea pigs, etc. Wait). In a preferred embodiment, the patient is a human. It is intended to include any subject participating in a clinical research trial as a patient. The patient may have been previously treated with, for example, other drugs and/or any KDM1A inhibitor. In one aspect, the patient has not been previously treated with any KDM1A inhibitor. The patient may be being treated with other drugs, especially at the time the sample is obtained, however, the patient should not be being treated with any KDM1A inhibitor at the time the sample is obtained for use in the methods according to the invention (ie, the patient is not being treated with any KDM1A inhibitor at the time the sample is obtained. should be concomitantly treated with a KDM1A inhibitor). Alternatively, if the biomarker levels are still likely to be modulated by the (remaining) KDM1A inhibitor during that time period (eg, if the biomarker levels have not returned to levels prior to prior treatment with (or administration of) the KDM1A inhibitor), The patient should then not be being treated with any KDM1A inhibitor during the said period of time prior to obtaining the sample. For example, the patient should not be being treated with any KDM1A inhibitor within two weeks, or more preferably within one month, prior to obtaining the sample. The latter is to avoid biomarker levels still being modulated by the (remaining) KDM1A inhibitor.

As used herein, the term "biomarker" or "marker" refers to a protein or polynucleotide whose expression or presence in or on mammalian tissues or cells can be detected by standard methods (or methods disclosed herein), and correlates with the sensitivity of mammalian cells or tissues to treatments comprising KDM1A inhibitors. Biomarkers according to the invention are ASCL1 and SOX2.

As used herein, the term "measuring" the level of a biomarker refers to experimentally determining the amount of the biomarker in a sample using an appropriate detection method as described elsewhere herein.

As used herein, the term "threshold" refers to a predetermined value, line, or more complex n-dimensional function that defines the boundary between two categories/subsets of a population, eg, the one that is more likely to respond to KDM1A inhibitor treatment SCLC patients relative to SCLC patients who are less likely to respond to KDM1A inhibitor therapy. In methods according to the invention, different thresholds can be applied to the levels of individual biomarkers (ie the biomarkers ASCL1 and SOX2 each have their respective thresholds) or to classification by using as described elsewhere herein Algorithm-derived scores from biomarker levels. As will be understood by those skilled in the art, thresholds are established to best differentiate between different classes of samples. Thresholds can be established according to methods known in the art. Typically, thresholds can be determined experimentally or theoretically using training set samples with known sensitivity or resistance to KDM1A inhibitor treatment. Training samples can be, for example, SCLC cell lines, patient-derived xenografts (PDX), or human clinical samples with known sensitivity or resistance to KDM1A inhibitor treatment. Thresholds may also be arbitrarily selected based on existing experimental and/or clinical and/or regulatory requirements, as will be recognized by one of ordinary skill in the art. Preferably, thresholds are established to obtain optimal sensitivity and specificity as a function of the test and the benefit/risk balance (false positive and false negative clinical outcomes). In general, the optimal sensitivity and specificity (and thresholds) can be determined using receiver operating characteristic (ROC) curves based on experimental data, as shown in the accompanying examples. In some embodiments, the threshold is a threshold value. In some embodiments, the cut-off values for the biomarkers are derived from ASCL1 and SOX2 levels measured in one or more samples of patient-derived SCLC cells that are responsive to treatment comprising a KDM1A inhibitor Sensitive or resistant. In some embodiments, the cut-off value is derived from ASCL1 and SOX2 levels measured in one or more samples of a (human) patient that responds or does not respond to a treatment comprising a KDM1A inhibitor. In some embodiments, the cutoff value is derived from ASCL1 and SOX2 levels measured in one or more samples obtained from a patient-derived xenograft model for treatment comprising a KDM1A inhibitor To respond or not to respond. In some embodiments, the cutoff value is obtained from the mRNA level of the biomarker. In some embodiments, the cutoff value is obtained from the protein level of the biomarker.

As used herein, the term "score" refers to the output calculated by a classification algorithm from biomarker levels measured in a sample. The score will be compared to/to a threshold and used to decide whether the patient from which the sample is derived is more or less likely to respond to a treatment comprising a KDM1A inhibitor. For example, when the score exceeds a threshold, a patient is identified as more likely to respond to treatment comprising a KDM1A inhibitor/patient is identified as he/she could benefit from treatment comprising a KDM1A inhibitor.

As used herein, a "classification algorithm" is a mathematical function that is used to calculate a score for a sample and to assess ("classify") which class the sample belongs to, ie whether it exceeds a threshold. Classification algorithms are well known in the art. Examples of classification algorithms include: Linear Classifier, Fisher Linear Discriminant, Linear Boolean Classification, Logistic Regression, Naive Bayes Classifier, Perceptron, SVM, Least Squares SVM, Quadratic Classifier , Kernel Estimation, k-Nearest Neighbors, Decision Trees, Random Forests, Neural Networks, Learning Vector Quantization. Software packages incorporating one or several classification algorithms are readily available or downloaded online, including, for example, DTREG, XLStat, http://www.support-vector-machines.org/SVM_soft.html, etc. In some embodiments, the classification algorithm is a Boolean function (truth function).

A sample can be classified using the Boolean conjunction function A AND B, where A and B assess whether the level of each of the biomarkers (ASCL1 and SOX2) in the sample is above the corresponding threshold for that biomarker. When all criteria are met (for example, if the level of each of the biomarkers (ASCL1 and SOX2) in the sample is above/exceeds the corresponding threshold for that biomarker), the score given by the Boolean conjunctive function is usually represented by 1 ( true value), or when one (or both) of the criteria is not met (for example, if the level of only one of each biomarker (ASCL1 and SOX2) in the sample is above/exceeded the corresponding threshold for that biomarker, or For each biomarker (ASCL1 and SOX2), when no biomarker level was above/exceeded the corresponding threshold for that biomarker), the score given by the Boolean conjunctive function was represented by 0 (false value). The threshold applied to the score generated by the Boolean algorithm to classify samples is 0, i.e. samples exceeding this threshold (i.e. score > 0) are classified as likely to respond to/could benefit from the inclusion of KDM1A inhibition drug treatment.

In some embodiments, the classification algorithm is a support vector machine (SVM). SVM is used for classification by spatially mapping the training dataset and constructing an N-dimensional hyperplane that optimally divides the sample data into two classes (e.g., KDM1Ai-sensitive and resistant) to serve as a threshold function . In addition to linear classification, SVM can also use kernel tricks to map sample data into a high-dimensional feature space for nonlinear classification. Then, using the trained SVM's scoring function, new data are mapped into the same space and predicted to belong to a class based on which side of the hyperplane they fall on. The performance of classification algorithms (including thresholds) can be further evaluated by comparing the classification predicted by the algorithm to experimental values, calculating true positives, false positives, true negatives and false negatives, as well as sensitivity, specificity, etc. Known training samples for KDM1Ai responsiveness/resistance were performed multiple rounds of training to tune model parameters and/or optimize performance.

As used herein, the term "exceeds" refers to a test sample, for example from The biomarker levels or scores of SCLC patient samples (sensitivity to KDM1Ai unknown) under consideration for treatment containing KDM1Ai will exceed or cross thresholds. In some embodiments, the threshold value is a cutoff value, and when the biomarker level or score is above its corresponding cutoff value, the biomarker level or score will exceed the threshold value (cutoff value). The comparison of the level of the biomarker or score in the sample to the corresponding threshold of the biomarker or score can be done in-house, manually or can be automated by a computer program.

As used herein, the term "sample" in relation to a patient sample used in the methods according to the invention may be a tumor sample (eg a biopsy sample, eg a biopsy sample from a primary or metastatic SCLC lesion), A body fluid or patient-derived cell line, a PDX sample ("PDX" means "patient-derived xenograft", ie, a human tumor grown in a mouse), or one or more exosomes. Preferably, the sample is enriched/enriched for tumor cells. A sample from a patient for carrying out the method according to the invention is obtained prior to initiation of KDM1A inhibitor treatment (i.e. in the absence of current KDM1A inhibitor treatment and/or not eg after prior KDM1A inhibitor treatment (administration of a KDM1A inhibitor) If the biomarker levels are within a time period that can still be modulated by the (remaining) KDM1A inhibitor) - eg, a sample from a patient for use in the methods according to the invention cannot be ) within two weeks, preferably within one month. Biopsy samples can be obtained by well-known techniques and can be fresh, or can be subjected to post-collection preparation and storage techniques (e.g. freezing, fixation and/or embedding, e.g. formalin fixation, paraffin embedding, fresh rapid frozen, fixed and frozen OCT embedded, etc.) processing. Body fluid samples can be obtained by well-known techniques, including samples of blood, sputum, bronchoalveolar lavage fluid, or any other bodily secretions or derivatives thereof that may contain SCLC cells. Isolated cells can be obtained from body fluids or tissues or organs by separation techniques such as centrifugation or cell sorting. Of course, various well-known post-collection preparation and storage techniques (eg, nucleic acid and/or protein extraction, fixation, storage, freezing, ultrafiltration, concentration, evaporation, centrifugation, etc.). Preferably, the sample from which the biomarker level is measured is enriched/enriched in the presence of SCLC cells or SCLC cell-derived vesicles (eg exosomes, etc.). For example, SCLC cells can be isolated from sputum using methods described in the literature (Chest. 1992 Aug; 102(2):372-4). SCLC circulating tumor cells (CTCs) can be obtained through literature (Peeters et al, Br JCancer 2013 Apr 2;108(6):1358-67; Hodgkinson et al, Nat Med. 2014 Aug;20(8): 897-903; Carter et al., Nat Med. 2017 Jan;23(1):114-119) purified from blood, SCLC-derived exosomes can be obtained by literature (Li et al., Theranostics. 2017;7(3):789-804; Sandfeld-Paulsen et al, J Thorac Oncol. 2016 Oct;11(10):1701-10) were purified from blood. Biomarker levels can also be analyzed from spatially defined regions of SCLC cell-enriched samples, which can be determined, for example, by an anatomical pathologist using standard methods used in the art.

In the context of a treatment comprising a KDM1A inhibitor, the terms "responsive", "responsive", "responsive", "sensitive", "sensitive", etc., mean that a SCLC patient (or sample, SCLC cell line, etc.) shows A positive response to KDM1A inhibition is a positive response to a treatment comprising a KDM1A inhibitor. In a more simplified form, the terms "responsive to treatment comprising a KDM1A inhibitor" and the like may be expressed as "responsive to a KDM1A inhibitor", "responsive to KDM1A inhibition", and the like. For example, "a positive response to treatment comprising a KDM1A inhibitor" or "benefiting from treatment comprising a KDM1A inhibitor" can be or can include reversing, alleviating or inhibiting the progression of the disease SCLC or one or more symptoms thereof. As used herein, the term "more likely to respond" can mean "more responsive" or simply "responsive."

The phrases "identifying a patient" or "selecting a patient" are used interchangeably and, as used herein, refer to the use of information or data generated related to biomarker levels in a patient sample to identify or select patients more likely to contain KDM1A inhibition. Patients who respond to (or benefit from treatment comprising a KDM1A inhibitor) or are less likely to respond to (or benefit from treatment comprising a KDM1A inhibitor) treatment with a KDM1A inhibitor. The information or data used or generated may be in any form, written, oral or electronic. The methods and uses provided herein can include communicating results, information, or data to a patient and/or anyone involved in or responsible for a patient's treatment comprising a KDM1A inhibitor. In some embodiments, the use of the generated information or data includes communicating, presenting, reporting, storing, sending, transferring, supplying, transmitting, distributing, or a combination thereof. In some embodiments, communicating, presenting, reporting, storing, sending, transferring, supplying, transmitting, distributing, or a combination thereof, is performed by a computing device, an analyzer unit, or a combination thereof. In some further embodiments, communicating, presenting, reporting, storing, sending, transferring, supplying, transmitting, distributing, or a combination thereof, is performed by a laboratory or medical professional. In some embodiments, the information or data includes a comparison of biomarker levels to thresholds. In some embodiments, the information or data includes an indication that the patient is more or less likely to respond to (or benefit from) treatment comprising a KDM1A inhibitor.

As used herein, the phrase "predicting a patient's responsiveness" refers to using the generated information or data related to biomarker levels in a patient sample to assess the likelihood that a patient will respond to a treatment comprising a KDM1A inhibitor. The information or data used or generated may be in any form, written, oral or electronic. In some embodiments, the use of the generated information or data includes communicating, presenting, reporting, storing, sending, transferring, supplying, transmitting, distributing, or a combination thereof. In some embodiments, communicating, presenting, reporting, storing, sending, transferring, supplying, transmitting, distributing, or a combination thereof, is performed by a computing device, an analyzer unit, or a combination thereof. In some further embodiments, communicating, presenting, reporting, storing, sending, transferring, supplying, transmitting, distributing, or a combination thereof, is performed by a laboratory or medical professional. In some embodiments, the information or data includes a comparison of biomarker levels to thresholds. In some embodiments, the information or data includes an indication that the patient is more or less likely to respond to treatment comprising a KDM1A inhibitor.

As used herein, the phrase "selecting a treatment" refers to identifying or selecting a treatment (therapy) for a patient using the generated information or data related to biomarker levels in a patient sample. In some embodiments, the treatment may comprise a KDM1A inhibitor. The information or data used or generated may be in any form, written, oral or electronic. In some embodiments, the use of the generated information or data includes communicating, presenting, reporting, storing, sending, transferring, supplying, transmitting, distributing, or a combination thereof. In some embodiments, communicating, presenting, reporting, storing, sending, transferring, supplying, transmitting, distributing, or a combination thereof, is performed by a computing device, an analyzer unit, or a combination thereof. In some further embodiments, communicating, presenting, reporting, storing, sending, transferring, supplying, transmitting, distributing, or a combination thereof, is performed by a laboratory or medical professional. In some embodiments, the information or data includes a comparison of biomarker levels to thresholds. In some embodiments, the information or data includes an indication that treatment comprising a KDM1A inhibitor is appropriate for the patient (ie, the patient is likely to respond to the treatment).

As used herein, the phrase "recommended treatment" refers to the use of generated information or data related to biomarker levels to identify or select patients who are more or less likely to respond to treatment comprising a KDM1A inhibitor Treatments containing KDM1A inhibitors. The information or data used or generated may be in any form, written, oral or electronic. In some embodiments, the use of the generated information or data includes communicating, presenting, reporting, storing, sending, transferring, supplying, transmitting, distributing, or a combination thereof. In some embodiments, communicating, presenting, reporting, storing, sending, transferring, supplying, transmitting, distributing, or a combination thereof, is performed by a computing device, an analyzer unit, or a combination thereof. In some further embodiments, communicating, presenting, reporting, storing, sending, transferring, supplying, transmitting, distributing, or a combination thereof, is performed by a laboratory or medical professional. In some embodiments, the information or data includes a comparison of biomarker levels to thresholds. In some embodiments, the information or data includes an indication that treatment comprising a KDM1A inhibitor is appropriate for the patient.

As used herein, a "kit" is any article of manufacture (eg, a package or container) comprising one or more reagents for measuring ASCL1 and SOX2 levels as described herein, the article of manufacture serving as a Units performing the method of the present invention are promoted, distributed or sold.

As used herein, "reagents" for measuring ASCL1 or SOX2 levels include any reagents commonly used in the art for measuring biomarker levels, including but not limited to specifically detecting biomarkers according to the present invention Antibodies that specifically recognize ASCL1 or SOX2 proteins, or probes and/or primers that hybridize to ASCL1 or SOX2 polynucleotides, and any other related to the methods for measuring biomarker levels described in greater detail elsewhere herein such reagents.

The present invention provides tools and methods for identifying SCLC patients who have an increased likelihood of responding to KDM1A inhibitor therapy and thus are most amenable to KDM1A inhibitor-containing therapy, and methods of treating these patients with KDM1A inhibitors . The present invention is based, at least in part, on the discovery that levels of ASCL1 and SOX2 can be used as biomarkers (eg, predictive biomarkers) in methods of predicting likelihood of response to KDM1A inhibitor therapy. As described herein and in the accompanying Examples, the inventors have found that high levels of ASCL1 and SOX2 in SCLC cell lines correlate with SCLC responsiveness (sensitivity) to KDM1A inhibitor treatment. As shown in Examples 2, 3 and 4 and Figures 1 to 3, SCLC cell lines expressing high levels of ASCL1 and SOX2 are generally responsive (sensitive) to KDM1A inhibitors, while exhibiting low levels of either ASCL1 and SOX2 or Both SCLC cell lines are generally resistant to KDM1A inhibitory therapy. Therefore, ASCL1 and SOX2 levels can be used to stratify KDM1A inhibitor-treated SCLC patients, identifying those more likely to respond to KDM1A inhibitor treatment and those less likely to respond to KDM1A inhibition. The method according to the present invention using ASLC1 and SOX2 is able to predict SCLC responsiveness to KDM1A inhibition with high sensitivity and specificity, as shown in more detail in the accompanying examples, which is a consequence of the reduced number of biomarkers used Very meaningful. The method according to the invention can be measured as a biomarker at the mRNA level or the protein level, since a good correlation is shown between its mRNA and protein expression levels, as in Example 5 using SCLC cell lines or patient-derived samples such as SCLC PDX samples shown. This makes the method according to the present invention particularly advantageous for use in clinical practice, especially in hospitals, since the samples most easily obtained from cancer patients are usually fixed tumor biopsies, which are more suitable for protein level analysis, on the grounds that Standard biopsy sample fixation procedures are known to fragment and reduce RNA.

As previously mentioned, the methods of the present invention comprise measuring the levels of ASCL1 and SOX2. These markers are themselves known in the art and are also described below.

Common database directories for ASCL1 and SOX2:

The DNA and protein sequences of human ASCL1 and SOX2 have been reported previously, see GenBank numbers listed below (NCBI-GenBank Flat File Release 225.0, April 15, 2018) and UniProtKB/Swiss-Prot numbers (Knowledgebase Release 2018_04, 2018 April 25, 2009), each of which is hereby incorporated by reference in its entirety for all purposes. Such sequences can be used to design procedures for measuring ASCL1 and SOX2 levels by methods known to those skilled in the art.

Exemplary nucleotide and amino acid sequences of human ASCL1 and SOX2 are set forth herein in SEQ ID NOs: 1-4. The following table assigns the markers and corresponding sequences:

In one aspect, the invention provides a method of identifying SCLC patients who are more likely to respond to treatment comprising a KDM1A inhibitor, the method comprising measuring ASCL1 and levels of SOX2. In some embodiments, a patient is identified as more likely to respond to treatment comprising a KDM1A inhibitor when the levels of each of ASCL1 and SOX2 in the sample exceed a threshold. In some embodiments, the method further comprises using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, wherein when the score in the sample exceeds a threshold, the patient is identified as being more likely to respond to a KDM1A inhibitor-containing treatment response. Accordingly, in some embodiments, the present invention provides a method of identifying SCLC patients more likely to respond to therapy comprising a KDM1A inhibitor, the method comprising measuring a sample from the patient prior to initiating therapy comprising a KDM1A inhibitor The levels of ASCL1 and SOX2 in the sample, wherein when the respective levels of ASCL1 and SOX2 in the sample exceed a threshold, the patient is identified as more likely to respond to a treatment comprising a KDM1A inhibitor. In some embodiments, the present invention provides a method of identifying SCLC patients who are more likely to respond to treatment comprising a KDM1A inhibitor, the method comprising measuring in a sample from the patient prior to initiating treatment comprising a KDM1A inhibitor The levels of ASCL1 and SOX2, and the levels of ASCL1 and SOX2 in the sample are used to generate a score for the sample, wherein when the score in the sample exceeds a threshold, the patient is identified as more likely to respond to treatment comprising a KDM1A inhibitor.

In another aspect, the invention provides a method of identifying SCLC patients who may benefit from treatment comprising a KDM1A inhibitor, the method comprising measuring ASCL1 and SOX2 in a sample from the patient prior to initiating treatment comprising a KDM1A inhibitor s level. In some embodiments, a patient is identified as a patient who may benefit from treatment comprising a KDM1A inhibitor when the level of each of ASCL1 and SOX2 in the sample exceeds a threshold. In some embodiments, the method further comprises using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, wherein when the score in the sample exceeds a threshold, the patient is identified as being able to benefit from treatment comprising a KDM1A inhibitor of patients. Accordingly, in some embodiments, the invention features a method of identifying SCLC patients who may benefit from treatment comprising a KDM1A inhibitor, the method comprising, prior to initiating treatment comprising a KDM1A inhibitor, measuring in a sample from the patient of ASCL1 and SOX2 levels, wherein when the respective levels of ASCL1 and SOX2 in the sample exceed a threshold, a patient is identified as being able to benefit from treatment comprising a KDM1A inhibitor. In some embodiments, the invention features a method of identifying SCLC patients who may benefit from treatment comprising a KDM1A inhibitor, the method comprising measuring ASCL1 in a sample from the patient prior to initiating treatment comprising a KDM1A inhibitor and levels of SOX2, and use the levels of ASCL1 and SOX2 in a sample to generate a score for that sample, wherein when the score in the sample exceeds a threshold, a patient is identified as being able to benefit from treatment comprising a KDM1A inhibitor.

In a further aspect, the invention provides a method of predicting the responsiveness of a SCLC patient to a treatment comprising a KDM1A inhibitor, the method comprising measuring ASCL1 and SOX2 in a sample from the patient prior to initiating treatment comprising a KDM1A inhibitor s level. In some embodiments, a patient is identified as being more likely to respond to treatment comprising a KDM1A inhibitor when the level of each of ASCL1 and SOX2 in the sample exceeds a threshold. In some embodiments, the method further comprises using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, wherein when the score in the sample exceeds a threshold, the patient is identified as being more likely to respond to a KDM1A inhibitor-containing Treatment responds. Accordingly, in some embodiments, the present invention provides a method of predicting the responsiveness of a SCLC patient to a treatment comprising a KDM1A inhibitor, the method comprising measuring in a sample from the patient prior to initiating treatment comprising a KDM1A inhibitor Levels of ASCL1 and SOX2, wherein when the respective levels of ASCL1 and SOX2 in the sample exceed a threshold, the patient is identified as more likely to respond to treatment comprising a KDM1A inhibitor. In some embodiments, the present invention provides a method of predicting the responsiveness of a SCLC patient to treatment comprising a KDM1A inhibitor, the method comprising measuring ASCL1 and The level of SOX2, and the levels of ASCL1 and SOX2 in the sample are used to generate a score for the sample, wherein when the score in the sample exceeds a threshold, the patient is identified as more likely to respond to treatment comprising a KDM1A inhibitor.

In a further aspect, the invention provides a method of assessing the likelihood of a SCLC patient's response to therapy comprising a KDM1A inhibitor, the method comprising measuring ASCL1 and SOX2 in a sample from the patient prior to initiation of therapy comprising a KDM1A inhibitor s level. In some embodiments, a patient is identified as more likely to respond to treatment comprising a KDM1A inhibitor when the levels of each of ASCL1 and SOX2 in the sample exceed a threshold. In some embodiments, the method further comprises using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, wherein when the score in the sample exceeds a threshold, the patient is identified as being more likely to respond to a KDM1A inhibitor-containing treatment response. Accordingly, in some embodiments, the present invention provides a method of assessing the likelihood of a SCLC patient's response to therapy comprising a KDM1A inhibitor, the method comprising, prior to initiation of therapy comprising a KDM1A inhibitor, measuring in a sample from the patient Levels of ASCL1 and SOX2, wherein when the respective levels of ASCL1 and SOX2 in the sample exceed a threshold, the patient is identified as more likely to respond to treatment comprising a KDM1A inhibitor. In some embodiments, the present invention provides a method of assessing the likelihood of a SCLC patient's response to a treatment comprising a KDM1A inhibitor, the method comprising measuring ASCL1 and The level of SOX2, and the levels of ASCL1 and SOX2 in the sample are used to generate a score for the sample, wherein when the score in the sample exceeds a threshold, the patient is identified as more likely to respond to treatment comprising a KDM1A inhibitor.

In a further aspect, the present invention provides a method of assessing the likelihood that a patient's SCLC will respond to therapy comprising a KDM1A inhibitor, the method comprising measuring ASCL1 in a sample from an SCLC patient prior to initiating therapy comprising a KDM1A inhibitor and SOX2 levels. In some embodiments, SCLCs are identified as more likely to respond to treatment comprising a KDM1A inhibitor when the levels of each of ASCL1 and SOX2 in the sample exceed a threshold. In some embodiments, the method further comprises using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, wherein when the score in the sample exceeds a threshold, SCLCs are identified as more likely to be sensitive to a KDM1A inhibitor-containing treatment response. Accordingly, in some embodiments, the present invention provides a method of assessing the likelihood of SCLC responding to a treatment comprising a KDM1A inhibitor, the method comprising measuring in a sample from an SCLC patient prior to initiation of treatment comprising a KDM1A inhibitor ASCL1 and SOX2 levels, where SCLCs were identified as more likely to respond to treatment comprising a KDM1A inhibitor when the respective levels of ASCL1 and SOX2 in the sample exceeded a threshold. In some embodiments, the present invention provides a method of assessing the likelihood of SCLC response to therapy comprising a KDM1A inhibitor, the method comprising measuring ASCL1 and ASCL1 in a sample from an SCLC patient prior to initiation of therapy comprising a KDM1A inhibitor The level of SOX2, and the levels of ASCL1 and SOX2 in a sample are used to generate a score for that sample, wherein when the score in the sample exceeds a threshold, SCLCs are identified as more likely to respond to treatment comprising a KDM1A inhibitor.

In a further aspect, the present invention provides a method of selecting therapy for a patient with SCLC, the method comprising measuring the levels of ASCL1 and SOX2 in a sample from the patient prior to initiating therapy. In some embodiments, when the level of each of ASCL1 and SOX2 in the sample exceeds a threshold, the method includes providing a recommendation that the treatment selected for the patient comprises a KDM1A inhibitor. In some embodiments, the method further comprises using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, and when the score in the sample exceeds a threshold, providing a recommendation that the treatment selected for the patient comprises a KDM1A inhibitor. Accordingly, in some embodiments, the present invention also provides a method of selecting a treatment for a patient with SCLC, the method comprising measuring the levels of ASCL1 and SOX2 in a sample from the patient prior to initiating treatment, and when the ASCL1 and SOX2 in the sample are When the respective levels exceed the thresholds, a recommendation is provided that the treatment selected for the patient includes a KDM1A inhibitor. In some embodiments, the present invention also provides a method of selecting a treatment for a patient with SCLC, the method comprising measuring the levels of ASCL1 and SOX2 in a sample from the patient prior to initiating treatment, using the levels of ASCL1 and SOX2 in the sample To generate a score for this sample, and when the score in the sample exceeds a threshold, a recommendation is provided that the treatment selected for the patient comprises a KDM1A inhibitor.

All of the above methods according to the invention comprise measuring the levels of the biomarkers of the invention (ASCL1 and SOX2) in a sample, and assessing said biomarker levels or derived scores (based on said levels) vs. thresholds. Typically, each biomarker (ie, ASCL1 and SOX2) has its own threshold (which can be established as described elsewhere herein), and the (measured) levels of ASCL1 and SOX2 are each assessed relative to their respective thresholds, where when the sample is A patient is identified as more likely to respond to treatment comprising a KDM1A inhibitor (or, if applicable, a patient is identified as a patient who could benefit from treatment comprising a KDM1A inhibitor, etc.) . Alternatively, the (measured) levels of ASCL1 and SOX2 in a sample can be used using a classification algorithm to generate a score for that sample; in this case, a score threshold (which can be established as described elsewhere herein) will be applied, and when the sample is When the score exceeds the threshold, the patient is identified as more likely to respond to treatment comprising a KDM1A inhibitor (or, if applicable, a patient is identified as a patient who could benefit from treatment comprising a KDM1A inhibitor, etc.).

In some embodiments of any of the preceding aspects, the method further comprises the step of obtaining or providing a sample from the patient. The obtaining/providing step precedes the measurement of the level of the biomarker (and prior to administration of any KDM1A inhibitor-containing treatment to the patient for whom the sample is to be obtained/provided).

In some embodiments of any of the preceding aspects, if the patient is identified as being more likely to respond to treatment comprising the KDM1A inhibitor, the method further comprises recommending, prescribing or administering to the patient a therapeutically effective amount of a KDM1A inhibitor comprising treat.

In some embodiments of any of the preceding aspects, if the patient is identified as unlikely to respond to therapy comprising the KDM1A inhibitor, the method may optionally further comprise recommending that the patient not be treated with the KDM1A inhibitor.

In the methods according to the invention, the levels of ASCL1 and SOX2 can be determined at the mRNA level or the protein level using any method known in the art for measuring mRNA or protein levels, including methods as described herein.

In the method according to the invention, the mRNA from the sample can be used directly to determine the level of the biomarker. In the method according to the invention, the level can be determined by hybridization. In the method according to the invention, the RNA can be converted into a cDNA (complementary DNA) copy using methods known in the art. Detection methods may include, but are not limited to, quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), gene expression analysis, RNA sequencing, nanopore sequencing, microarray analysis, gene expression chip analysis, (in situ) hybridization techniques, RNAscope and Chromatography and any other technique known in the art, such as those described in Ralph Rapley, "The Nucleic Acid Protocols Handbook", published 2000, ISBN: 978-0-89603-459-4. Methods for detecting RNA can include, but are not limited to, PCR, real-time PCR, digital PCR, hybridization, microarray analysis, and any other technique known in the art, for example in Leland et al., "Handbook of Molecular and cellular Methods in Biology and Medicine", published in 2011, those described in ISBN9781420069389.

/荧光共振能量转移(FRET)、差示扫描荧光法(DSF)、微流体、分光光度法、质谱法、酶测定法、表面等离子共振或其组合。免疫测定法可以是均相测定法或异相测定法。在均相测定法中，免疫反应通常包括特异性抗体、标记的分析物和目的样品。在抗体与标记的分析物结合后，直接或间接地修饰由标记产生的信号。免疫反应及其程度检测都可以在均质溶液中进行。可以使用的免疫化学标记包括自由基、放射性同位素、荧光染料、酶、噬菌体或辅酶。在异相测定法中，试剂通常是样品、抗体和产生可检测信号的工具。抗体可以固定在支持物，例如珠子、平板或载玻片上，并且与液相中疑似含有抗原的样本接触。然后将支持物与液相分离，并使用产生信号的工具检查支持物相或液相的这种可检测信号。该信号与样品中分析物的存在有关。产生可检测信号的工具包括使用放射性标记、荧光标记或酶标记。In the method according to the invention, the method may comprise detecting the protein expression level of the biomarker. Any suitable protein detection, quantification and comparison methods can be used, such as those described in John M. Walker, "The Protein Protocols Handbook", published 2009, ISBN 978-1-59745-198-7. Protein expression levels of biomarkers can be determined by immunoassays involving the recognition of proteins or protein complexes by antibodies or antibody fragments, including but not limited to enzyme-linked immunosorbent assay (ELISA), "sandwich" Immunoassays, immunoradiometric assays, in situ immunoassays, alphaLISA immunoassays, protein proximity assays, proximity ligation assay techniques (e.g. protein qPCR), Western blot analysis, immunoprecipitation assays, immunofluorescence assays, flow cytometry, immunohistochemistry (IHC), immunoelectrophoresis, protein immunostaining, confocal microscopy; or by similar methods in which antibodies or antibody fragments are chemically probed, aptamers, receptors, interacting proteins or in a specific manner any other biomolecular substitution that recognizes the biomarker protein; or

/Fluorescence resonance energy transfer (FRET), differential scanning fluorescence (DSF), microfluidics, spectrophotometry, mass spectrometry, enzymatic assays, surface plasmon resonance, or a combination thereof. The immunoassay can be a homogeneous assay or a heterogeneous assay. In a homogeneous assay, the immune response typically includes a specific antibody, a labeled analyte, and a sample of interest. Upon binding of the antibody to the labeled analyte, the signal produced by the label is directly or indirectly modified. Both the immune response and its extent can be detected in a homogeneous solution. Immunochemical labels that can be used include free radicals, radioisotopes, fluorescent dyes, enzymes, bacteriophages, or coenzymes. In heterogeneous assays, the reagents are typically the sample, the antibody, and the means to generate a detectable signal. Antibodies can be immobilized on a support, such as a bead, plate, or slide, and contacted with a sample suspected of containing the antigen in the liquid phase. The support is then separated from the liquid phase, and the support phase or liquid phase is examined for this detectable signal using a signal generating tool. This signal is related to the presence of the analyte in the sample. Tools for generating a detectable signal include the use of radioactive, fluorescent, or enzymatic labels.

In the method according to the invention, antibodies directed against the biomarker of interest can be used. In the method according to the invention, a kit for detection can be used. Such antibodies and kits are available from commercial sources such as EMDMillipore, R&D Systems for Biochemical Assays, Thermo Scientific Pierce Antibodies, Novus Biologicals, Aviva Systems Biology, Abnova Corporation, AbDSerotec, or others. Alternatively, antibodies can also be synthesized by any known method. As used herein, the term "antibody" is intended to include monoclonal antibodies, polyclonal antibodies, single chain antibodies and chimeric antibodies. Antibodies can be bound to a suitable solid support (eg, beads, microspheres, plates, glass slides, such as protein A or protein G agarose, or beads such as latex or polystyrene) according to known techniques, such as passive conjugation. the hole formed by the material) conjugation. Antibodies as described herein can also be conjugated with detectable labels or capable of generating signals such as radioactive labels (eg, ³⁵ S), enzymatic labels (eg, horseradish peroxidase, alkaline phosphatase), fluorescent labels (eg, fluorescent labels protein, Alexa, green fluorescent protein, rhodamine), phthalocyanine-containing beads that can release singlet oxygen upon 680 nM irradiation and excite the emission of light after its subsequent absorption by europium- or erbium-containing acceptor beads, and oligonuclei nucleotide-labeled group conjugation. Labels can generate signals directly or indirectly. The generated signal may include, for example, fluorescence, radioactivity or luminescence, according to known techniques.

Antibodies can be replaced with alternative protein capture agents including aptamers, affimers or chemoprobes that have high affinity and selectivity for the protein biomarker to be analyzed.

In some embodiments, such as in immunofluorescence analysis of biopsies, when the protein levels of the biomarkers are measured, the levels of the biomarkers can be assessed in a portion of SCLC tumor cells, such as in the nuclei of tumor cells.

Levels of biomarkers can be expressed as any of the mRNA expression measures or protein expression measures used in the art, and can be raw data or processed data, i.e., raw data background subtracted, normalized or commonly used in the art other corrections or other mathematical transformations. For example, when measured by microarray hybridization, biomarker levels can be expressed as the value of the hybridization signal intensity of the sample, Log2(the value of the hybridization signal intensity of the sample), or Log2(the value of the hybridization signal intensity of the sample/the value of the hybridization signal intensity of the reference sample) . Examples of suitable reference samples are patient-derived tumor samples with high expression levels of ASCL1 and SOX2 obtained from xenografts or PDX models or SCLC cell pellets. Hybridization signal values can be obtained using single-color or 2-color hybridization. For example, when measured by qRT-PCR, biomarker levels can be expressed as the intersection-PCR-cycle (Cp) value (expressed as the number of cycles required to reach a specific detection threshold level of amplification-related fluorescence), ΔCp=C _p – C _{p, reference gene} , 2 ^-Cp value or 2 ^-ΔCp value. For example, when measured by RNA sequencing, biomarker levels can be expressed as Reads Per Million (RPM), Reads PerKilobase Million (RPKM), Reads Per Kilobase Million (RPKM), Fragments Per Kilobase Million (FPKM) or Transcripts Per Million (TPM) values. For example, when measured by Western blotting, biomarker levels can be expressed as the integrated density (AU) of the corresponding band after image analysis, either as raw integrated density or normalized to protein content and/or as relative to a reference ratio of samples. For example, when measured by immunostaining, biomarker levels can be expressed as the integrated density (AU) of nuclear signal after image analysis, as raw integrated density (AU) per area unit (pixel ² or μm ² ), or as integrated density /nucleus, or as a ratio relative to a reference sample. For example, when measured by ELISA, biomarker levels can be expressed as RLU (Relative Light units) or absorbance units.

In some embodiments of any of the methods according to the invention, the biomarker levels (ie, ASCL1 levels and SOX2 levels) are mRNA expression levels. Preferably, mRNA expression levels are measured by qRT-PCR.

In some embodiments of any of the methods according to the invention, the biomarker level is a protein expression level. Preferably, protein expression levels are measured by fluorescent immunohistochemistry.

In some embodiments, biomarker expression levels in immunofluorescence staining are visually classified as high, moderate, low, or undetectable based on staining intensity levels, with values of 3, 2, 1, and 0, respectively. Samples with neutralized high levels (values 2 and 3) were considered "positive" (i.e. above the threshold for the corresponding biomarker), while samples with undetectable or low levels (values 0 and 1) were considered "negative" ( i.e. not exceeding the threshold of the corresponding biomarker). When the sample was considered "positive" for both biomarkers (ie when the ASCL1 and SOX2 levels in the sample were classified as level 2 or 3, respectively), the patient was identified as more likely to be responsive to a KDM1A inhibitor-containing Treatment Response/Patient identified as he/she could benefit from treatment comprising a KDM1A inhibitor.

Alternatively, in some embodiments, the expression levels of biomarkers in immunofluorescent stained images can be quantified. DNA dyes such as DAPI staining can be used to localize nuclei using fluorescence quantification. SOX2 and ASCL1 expression in the nucleus can be quantitatively analyzed using immunofluorescence. Individual images from biomarker and DAPI staining can be analyzed using imaging software, eg, ImageJ. Signals can be obtained by background subtraction and normalization to a reference (calibration) sample. A suitable calibration sample is one with high and uniform nuclear expression of both biomarkers, such as NCI-H1417 derived xenograft samples. Normalized quantitative values can be expressed as %, or as a ratio relative to a calibration sample. Thresholds for each biomarker can be established as a fraction of the calibration sample signal, and should be chosen above the negative control sample used (which can be, for example, normal lung biopsies in which both biomarkers are low-expressing or undetectable). (average) signal of tissue examination samples or xenograft samples). Preferably, the threshold is at least the mean signal of the negative control samples plus 1 SD, 2 SD or 3 SD, where SD means standard deviation.

In some embodiments of the methods and uses according to the invention, the terms "when the level of each of ASCL1 and SOX2 in the sample exceeds a threshold" etc. may mean "when the level of each of ASCL1 and SOX2 in the sample increases compared to the control" . In this case, the patient is identified as more likely to respond to treatment comprising a KDM1A inhibitor when the levels of each of ASCL1 and SOX2 in the sample are increased compared to controls/patient is identified as he/she could benefit from the inclusion of KDM1A inhibitor therapy. The terms "control" or "reference" are used interchangeably herein. A non-limiting example of a "control" (eg "control value") or "reference" (eg "reference value") may be ASCL1 and SOX2 in a sample or pool of samples from one or more healthy individuals/subjects, respectively Level. For example, a healthy individual/subject may be an individual/subject who does not have SCLC as defined herein, in particular an individual/subject who does not have SCLC at the time the sample is obtained from the individual/subject. Alternatively, for example, a healthy individual/subject may be an individual/subject who does not have a disease or disorder associated with elevated levels of each of ASCL1 and SOX2. Preferably, the healthy individual/subject is a human. Another non-limiting example of a "control" (eg, a "control value") or a "reference" (eg, a "reference value)," respectively, may be from a "non-responder," eg, from one or more patients with SCLC who are known to have ASCL1 and SOX2 levels in samples or pools of patients who did not respond to KDM1A inhibitors. Another example of a "non-responder" control is a cell line/cell/tissue that shows no response to a KDM1A inhibitor in an in vitro, ex vivo or (patient derived) xenograft assay. Another non-limiting example of a "control" is an "internal standard" such as purified or synthetically produced proteins and/or peptides or mixtures thereof, or corresponding nucleic acids, wherein each protein/ The amount of peptide (or corresponding nucleic acid). In particular, the "internal standard" may comprise the proteins ASCL1 and SOX2 (or corresponding nucleic acids) as described and defined herein. A "control" if, for example, the sample was obtained before the patient had SCLC, before the patient had a predisposition (or risk) for having SCLC cancer, or if the sample was obtained when the patient (completely) recovered from previous SCLC Non-limiting examples of (eg "control value") or "reference" (eg "reference value") may be the levels of ASCL1 and SOX2, respectively, in a sample from a patient to be identified herein.

In some embodiments of any of the preceding aspects, the sample is a SCLC biopsy sample, preferably an SCLC cell-enriched SCLC biopsy sample.

Preferably, in any of the methods of the invention, the patient is a human patient.

Herein preferably, the above-mentioned (diagnostic) method is an in vitro method. As used herein, "in vitro" refers to the methods of the invention as described above, such as methods of identifying SCLC patients more likely to respond to treatment comprising a KDM1A inhibitor, etc., not performed in vivo, ie not performed directly on the patient, Rather, it is performed in vitro in a living human (or other mammal), on samples obtained from and separated/isolated from the patient (ie removed from its in vivo location).

In a further aspect, the invention provides the use of ASCL1 and SOX2 in a method of identifying SCLC patients who are more likely to respond to treatment comprising a KDM1A inhibitor.

In a further aspect, the present invention provides the use of ASCL1 and SOX2 in a method of assessing the likelihood of a SCLC patient's response to therapy comprising a KDM1A inhibitor.

In a further aspect, the invention provides the use of one or more reagents for measuring levels of ASCL1 and SOX2 in a method of identifying SCLC patients who are more likely to respond to treatment comprising a KDM1A inhibitor.

In a further aspect, the present invention provides the use of one or more reagents for measuring the levels of ASCL1 and SOX2 in a method of assessing the likelihood of a SCLC patient's response to therapy comprising a KDM1A inhibitor.

In a further aspect, the present invention provides the use of ASCL1 and SOX2 in the manufacture of a diagnostic for identifying SCLC patients more likely to respond to treatment comprising a KDM1A inhibitor.

In a further aspect, the present invention provides the use of ASCL1 and SOX2 in the manufacture of a diagnostic agent for assessing the likelihood of a SCLC patient's response to therapy comprising a KDM1A inhibitor.

In another aspect, the invention provides a kit for identifying SCLC patients more likely to respond to treatment comprising a KDM1A inhibitor, the kit comprising one or more for measuring ASCL1 and SOX2 in a sample level of reagents, and optionally, instructions for use.

In another aspect, the invention provides a kit for assessing the likelihood of a SCLC patient's response to a treatment comprising a KDM1A inhibitor, the kit comprising one or more for measuring the levels of ASCL1 and SOX2 in a sample The reagents and, optionally, instructions for use.

Using the methods described above, it is possible to identify a subgroup of SCLC patients with a higher chance of response, ie, more likely to respond to or benefit from receiving treatment comprising KDM1Ai. The present invention also relates to therapeutic methods for treating those SCLC patients identified through this.

Accordingly, in a further aspect, the present invention provides a method of treating a patient with SCLC if, prior to initiating therapy comprising a KDM1A inhibitor, the patient has been identified as being more likely to have a a therapeutic response to the agent, the method comprises administering to the patient a therapeutically effective amount of a treatment comprising a KDM1A inhibitor.

In a further aspect, the invention features a method of treating a patient with SCLC, the method comprising measuring the levels of ASCL1 and SOX2 in a sample from the patient prior to initiating treatment, when the levels of each of ASCL1 and SOX2 in the sample exceed a threshold value , the patient is identified as being more likely to respond to a treatment comprising a KDM1A inhibitor, and if the patient is identified as being more likely to respond, a therapeutically effective amount of a treatment comprising a KDM1A inhibitor is administered to the patient.

In a further aspect, the invention features a method of treating a patient with SCLC, the method comprising measuring the levels of ASCL1 and SOX2 in a sample from the patient prior to initiation of treatment, using these levels to generate a score for the sample, when the sample is A patient is identified as more likely to respond to a treatment comprising a KDM1A inhibitor when the score in <RTI ID=0.0>in </RTI> exceeds a threshold, and if the patient is identified as more likely to respond, the patient is administered a therapeutically effective amount of a treatment comprising a KDM1A inhibitor.

In a further aspect, the invention provides a KDM1A inhibitor for use in the treatment of a patient with SCLC, wherein the patient has been identified as more likely to be more likely to be susceptible to a KDM1A inhibitor-containing treatment using a method according to any of the preceding aspects prior to initiation of treatment comprising a KDM1A inhibitor. treatment response to the drug.

In some embodiments of any of the preceding aspects, the method further comprises the step of obtaining or providing a sample from the patient.

Preferably, in any of the treatment methods and uses according to the present invention, the patient is a human patient.

Elsewhere in this disclosure relating to methods of measuring biomarker levels, sample types, etc., apply equally to the above-described methods of treatment and related therapeutic uses.

KDM1A inhibitors that can be used in accordance with the present invention include any KDM1A inhibitor currently known in the art or reported in the future. Preferably, the KDM1A inhibitor is a small molecule. Irreversible and reversible inhibitors of KDM1A have been reported. Irreversible KDM1A inhibitors exert their inhibitory activity by covalently binding to FAD cofactors within the KDM1A active site and are typically based on 2-cyclyl-cyclopropylamino moieties such as 2-(hetero)arylcyclopropyl amino moiety. Reversible inhibitors of KDM1A have also been disclosed. Preferably, the KDM1A inhibitor should be active in cells. For example, the cellular activity of KDM1A inhibitors can be determined using established in vitro cellular assays for KDM1A inhibitors, such as the SCLC cell viability assay (eg, as described in Example 1 herein or the assays described in Mohammad et al., 2015, Ibid) or acute myeloid leukemia cell line differentiation assays (eg, as in Lynch et al., Anal Biochem. 2013 Nov 1;442(1):104-6.doi:10.1016/j.ab.2013.07.032 assay described in).

Examples of KDM1A inhibitors that can be used according to the present invention include, but are not limited to, those disclosed in: WO2010/043721, WO2010/084160, WO2011/035941, WO2011/042217, WO2011/131697, WO2012/013727, WO2012/013728, WO2012/ 045883、WO2013/057320、WO2013/057322、WO2010/143582、WO2011/022489、WO2011/131576、WO2012/034116、WO2012/135113、WO2013/022047、WO2013/025805、WO2014/058071、WO2014/084298、WO2014/086790、 WO2014/164867、WO2014/205213、WO2015/021128、WO2015/031564、WO2007/021839、WO2008/127734、WO2014/164867WO2015/089192、WO2015/123408、WO2015/123424、WO2015/123437、WO2015/123465、WO2015/156417、 WO2015/181380、WO2016/123387、WO2016/130952、WO2016/172496、WO2016/177656、WO2017/027678、WO2012/071469、WO2013/033688、WO2014/085613、WO2015/120281、WO2015/134973、WO2015/168466、WO2015/ 200843、WO2016/003917、WO2016/004105、WO2016/007722、WO2016/007727、WO2016/007731、WO2016/007736、WO2016/034946、WO2016/037005、WO2016/161282、WO2016172496WO2017/004519、WO2017/027678、WO2017/079476、 WO2017/079670, WO2017/090756, WO2017/109061, WO2017/116558, WO2017/114497, WO2017/149463, WO2017/157322, WO2017/195216, WO2017/198780, WO2017/254 018/081342、WO2018/081343、US2010-0324147、US2015-0065434、US2017-0283397、CN106045862、CN104119280、CN103961340、CN103893163、CN103319466、CN103054869、CN105985265、CN106432248、CN106478639、CN106831489、CN106928235、CN107033148 CN107174584、CN107176927、CN107474011、CN107501169 and CN107936022, and

或者

or

Including any optically active stereoisomer or any pharmaceutically acceptable salt thereof.

A particularly preferred KDM1A inhibitor is (trans)-N1-((1R,2S)-2-phenylcyclopropyl)cyclohexane-1,4-diamine [CAS Reg. No. 1431304-21-0] or Its pharmaceutically acceptable salt, more preferably (trans)-N1-((1R,2S)-2-phenylcyclopropyl)cyclohexane-1,4-diamine dihydrochloride [CAS registration number 1431303-72-8]. Compound (trans)-N1-((1R,2S)-2-phenylcyclopropyl)cyclohexane-1,4-diamine, also named as (1r,4S)-N1-((1R, 2S)-2-phenylcyclopropyl)cyclohexane-1,4-diamine, known as ORY-1001 or iadademstat, has the chemical structure shown below:

ORY-1001 has been disclosed, for example, in WO2013/057322, see Example 5 therein. Pharmaceutical formulations comprising ORY-1001 administered to a patient can be prepared according to methods known to those skilled in the art, eg as described in WO2013/057322.

A KDM1A inhibitor can be administered as a single API, ie, as a monotherapy, or can be administered in combination with one or more other APIs, such as other anticancer agents used to treat SCLC.

Although KDM1A inhibitors (or treatments comprising KDM1A inhibitors) can be administered directly for therapy, typically KDM1A inhibitors are administered in the form of a pharmaceutical composition comprising the compound as an active pharmaceutical ingredient and one or more a pharmaceutically acceptable excipient or carrier. Any reference herein to a KDM1A inhibitor includes reference to the compound itself, ie, the corresponding compound in its non-salt form (eg, as a free base) or any pharmaceutically acceptable salt or solvate form thereof, as well as reference to the compound itself. A pharmaceutical composition comprising the compound (or a pharmaceutically acceptable salt or solvate thereof) and one or more pharmaceutically acceptable excipients or carriers.

KDM1A inhibitors can be administered in any manner that achieves the intended purpose. Examples include administration by oral or parenteral (including eg intravenous or subcutaneous) routes.

For oral delivery, the compounds can be incorporated into formulations comprising pharmaceutically acceptable carriers such as binders (eg, gelatin, cellulose, tragacanth), excipients (eg, starch, lactose), Lubricants (eg, magnesium stearate, silicon dioxide), disintegrants (eg, alginates, Primogel, and cornstarch), and sweetening or flavoring agents (eg, glucose, sucrose, saccharin, salicylic acid) methyl ester and mint). The formulations can be delivered orally, eg, in the form of closed gelatin capsules or compressed tablets. Capsules and tablets can be prepared by any conventional technique. Capsules and tablets may also be coated with a variety of coatings known in the art to improve the flavor, taste, color and shape of the capsules and tablets. In addition, liquid carriers such as fatty oils can also be included in the capsules.

Suitable oral formulations may also be in the form of suspensions, syrups, chewing gums, wafers, elixirs and the like. Conventional agents for improving the flavor, taste, color and shape of the particular form may also be included, if desired. Additionally, for the convenience of administration by enteral feeding tube to patients unable to swallow, the active compound can be dissolved in an acceptable lipophilic vegetable oil carrier, such as olive oil, corn oil, and safflower oil.

The compounds may also be administered parenterally in the form of solutions or suspensions, or may also be in lyophilized forms which can be converted to solutions or suspensions prior to use. In such formulations, diluents or pharmaceutically acceptable carriers such as sterile water and physiological saline buffer may be used. Other conventional solvents, pH buffers, stabilizers, antimicrobials, surfactants and antioxidants can be included. For example, useful components include sodium chloride, acetate, citrate or phosphate buffers, glycerol, dextrose, fixed oils, methylparaben, polyethylene glycol, propylene glycol, sodium bisulfate, benzene Methanol, ascorbic acid, etc. Parenteral preparations can be stored in any conventional containers, such as vials and ampoules.

Pharmaceutical compositions, such as oral and parenteral compositions, can be formulated in unit dosage form for ease of administration and uniformity of dosage. As used herein, "unit dosage form" refers to physically discrete units suitable as unitary dosages for administration to a subject, each unit containing a predetermined quantity of active ingredient calculated to produce the desired therapeutic effect, in association with one or more suitable pharmaceutical carriers.

In therapeutic applications, the pharmaceutical compositions will be administered in a manner appropriate to the disease to be treated, as determined by those skilled in the medical arts. Appropriate dosages and appropriate duration and frequency of administration will depend on factors such as the patient's state, the severity of the disease, the particular KDM1A inhibitor administered, the method of administration, and the judgment of the attending physician, among others. Generally, suitable dosages and administration regimens provide pharmaceutical compositions in amounts sufficient to provide therapeutic benefit, for example, improved clinical outcomes, such as more frequent complete or partial remissions, or longer disease-free and/or Overall survival, or reduction in symptom severity, or any other objectively identifiable improvement noted by the clinician. Effective doses can generally be estimated or extrapolated using experimental models such as dose-response curves derived from in vitro or animal model test systems. Those skilled in the art would be able to determine appropriate dosages and treatment regimens based on the factors described above.

The pharmaceutical compositions of the present invention can be included in a container, pack or dispenser with instructions for administration.

Example

The following examples are provided to illustrate the invention. They should not be construed as limiting the scope of the invention, but only as representative of the invention.

Example 1: Classification of KDM1A inhibitor-sensitive and resistant cell lines

测定法根据制造商的方案(Promega)评估细胞活力。使用Microsoft Excel软件计算EC50值，针对未处理的细胞(100％生长)和无

试剂(100％生长抑制)进行标准化。暴露于ORY-1001 10天后，在96孔板形式中进行活力定量(最大浓度为1μM，NCI-H187细胞系为100nM)。细胞最初接种于100μL培养基(RPMI-1640 10％FBS 2mM谷氨酰胺，除了NCI-H1876细胞系在HITES培养基中培养以外，通过向DMEM：F12 5％FBS中加入4.5mM谷氨酰胺、0.005mg/mL胰岛素、0.01mg/mL转铁蛋白、30nM亚硒酸钠、10nM氢化可的松、10nMβ-雌二醇制备)中，并在37℃和5％CO₂-受控气氛中孵育。在第6天，加入了额外的100μL含有ORY-1001的培养基。再过4天后，使用Alamar

测定法(Thermo Fisher Scientific)测量剩余活力。背景减除后，针对载体处理的细胞进行标准化。使用GraphPad Prism软件拟合非线性模型后，计算EC50值。For biomarker identification, seven SCLC cell lines were evaluated for their response to KDM1A inhibitor treatment based on viability assay results performed after 4 or 10 days of treatment with the KDM1A inhibitor ORY-1001 (as described elsewhere herein). Classification. For the 4-day viability assay, the SCLC cell lines were seeded in 384-well plates in a final volume of 40 μL (maximum concentration: 50 μM; tested 18 in the supplier-recommended optimized medium supplemented with ORY-1001 for each cell line). a series of 1:2 dilutions). After 4 days of incubation at 37°C in a 5% CO ₂ -controlled atmosphere, use

Assay Cell viability was assessed according to the manufacturer's protocol (Promega). EC50 values were calculated using Microsoft Excel software for untreated cells (100% growth) and no

Reagents (100% growth inhibition) were normalized. After 10 days of exposure to ORY-1001, viability quantification was performed in a 96-well plate format (maximum concentration of 1 μM, 100 nM for NCI-H187 cell line). Cells were initially seeded in 100 μL of medium (RPMI-1640 10% FBS 2 mM glutamine, except that the NCI-H1876 cell line was grown in HITES medium by adding 4.5 mM glutamine, 0.005 mM glutamine to DMEM:F12 5% FBS mg/mL insulin, 0.01 mg/mL transferrin, 30 nM sodium selenite, 10 nM hydrocortisone, 10 nM beta-estradiol) and incubated at 37°C in a 5% CO ₂ -controlled atmosphere. On day 6, an additional 100 μL of medium containing ORY-1001 was added. After another 4 days, use Alamar

Assay (Thermo Fisher Scientific) to measure residual viability. After background subtraction, normalization was performed for vehicle-treated cells. EC50 values were calculated after fitting a nonlinear model using GraphPad Prism software.

Cell lines were classified as sensitive to KDM1A inhibition when growth inhibition after treatment with ORY-1001 was equal to or greater than 25% and EC50 was below 10 nM. SHP77 was classified as partially sensitive because, although it was not sensitive under the conditions tested here, it was reported to be sensitive to ORY-1001 in other assays. The results obtained are shown in Table 1 below. The concentration that achieves a 50% reduction in growth (EC50) and the percent maximum growth inhibition (% maximal response) at the highest dose tested are reported in the table.

Table 1:

Example 2 Identification of ASCL1 and SOX2 as biomarkers of responsiveness to KDM1A inhibitors

To identify genes that can differentiate SCLC cell lines sensitive and resistant to KDM1A inhibitor treatment, four identified as KDM1Ai sensitive, one identified as KDM1Ai partially sensitive and two identified as KDM1Ai resistant SCLC cell lines were subjected to RNASeq analysis. Details on their responsiveness and classification to KDM1Ai are provided in Example 1 above.

Cell pellets for gene expression analysis

Cells were continuously grown in flasks for 6 days. On the last day of the assay, cells were collected in Falcon tubes, counted, and centrifuged at 1200 rpm for 4 minutes. The supernatant was discarded, cells were suspended in 1 mL of PBS and transferred to eppendorf samples, centrifuged at 3000 rpm for 5 min in an eppendorf centrifuge at 4 °C. Finally, the supernatant was discarded and the pellet was frozen at -80°C.

RNA sequencing

Total RNA was isolated from samples using the QIAGEN miRNeasy Mini kit. RNA isolation was performed using the QIAgen RNeasyMini kit. The RNeasy Mini kit combines the selective binding properties of silica-based membranes with the speed of microspin technology. Briefly, tissues were homogenized and lysed in RLT buffer (containing β-mercaptoethanol) using Lysing Matrix D tubes (MPBiomedicals LLC) or vortexing. Ethanol is added to the homogenate to provide suitable binding conditions for all RNA molecules longer than 200 nucleotides (nt). The sample is loaded onto an RNeasy Mini column, where total RNA is bound to the membrane and contaminants are efficiently washed away. High-quality RNA is then eluted with nuclease-free water. The resulting RNA was quantified using the Agilent Bioanalyzer and assessed for integrity.

Library generation was accomplished using the Illumina TruSeq Stranded mRNA Library Preparation. Cluster generation and sequencing of libraries were performed on Illumina HiSeq. Total RNA samples were converted into cDNA libraries using the TruSeq Stranded mRNA Sample Prep Kit (Illumina). Starting from 100 ng of total RNA, polyadenylated RNA (primarily mRNA) was selected and purified using oligo-dT-conjugated magnetic beads. This mRNA was chemically fragmented and converted to single-stranded cDNA using reverse transcriptase and random hexamer primers, and actinomycin D was added to inhibit DNA-dependent synthesis of the second strand. Double-stranded cDNA is generated by removing the RNA template and synthesizing the second strand in the presence of dUTP instead of dTTP. A single A base was added to the 3' end to facilitate ligation of the sequencing adapter, which contained a single T base overhang. The adaptor-ligated cDNA was amplified by polymerase chain reaction to increase the amount of sequence-ready library. During this amplification, the polymerase stops when it encounters a U base, making the second strand a poor template. Therefore, the amplification material uses the first strand as a template, thereby preserving the information of this strand. The size distribution of the final cDNA library was analyzed using an Agilent Bioanalyzer (DNA 1000 kit), quantified by qPCR (KAPA library quantification kit), and then normalized to 2 nM in preparation for sequencing. Standard Cluster Generation Kit v5 (Standard Cluster Generation Kit v5) binds the cDNA library to the flow cell surface. cBot isothermally amplifies the ligated cDNA constructs to yield clonal clusters of approximately 1000 copies each. DNA sequences were directly determined using sequencing-by-synthesis technology with the TruSeq SBS kit.

The following quality control metrics were applied: samples had 100 ng of input RNA and RIN values ≥ 7.0 to facilitate library preparation. A total of at least 30 million paired-end reads of 50 bp were generated per single sample. After subtracting multiple off-target sequences such as ribosomal RNA, phiX, homopolymer repeats, and globin RNA, at least 28.5 million reads were provided.

Illumina HiSeq software reports the total number of clusters (DNA fragments) loaded in each lane, the percentage that passed the sequencing quality filter (identifying errors due to overload and sequencing chemistry), the phred quality score for each base for each sequence read, the percentage of each Overall mean phred score for sequencing cycles, and overall percent error (based on alignment to the reference genome). For each RNA-seq sample, the percentage of reads containing mitochondrial and ribosomal RNA was calculated. The FASTQC package was used to provide additional QC metrics (base distribution, sequence repeats, overrepresented sequences and enriched kmers) and graphical summaries. Raw reads were aligned to the human genome (hg19) using GSNAP and the recommended RNASeq data option. In addition to genome sequences, GSNAP provides a database of human splice junctions and transcripts based on Ensembl v73. The resulting SAM files were then converted into classified BAM files using Samtools. Gene expression values were calculated as RPKM values and read counts according to Mortazavi et al. (Nat Methods (2008) 5(7):621-8). Normalized read counts were obtained using the R package DESeq2. Data are reported as the mean of Log2 (RPKM) of three independent experiments. RPKM stands for reads per kilobase per million.

result

Gene expression of ASCL1 and SOX2 in SCLC cell lines measured by RNASeq are shown in Table 2 below. The table shows the mean (Av.) and standard deviation (SD) in Log2 (RPKM). The color codes displayed are based on gene expression levels; the darker the color, the higher the expression of the biomarker. For comparison, GAPDH expression was reported in parallel to show stable expression of this reference gene across samples.

Table 2:

These results are represented graphically in Figure 1, which is a dot plot representing ASCL1 (Y-axis) of SCLC cell lines sensitive, partially sensitive or resistant to KDM1A inhibition as described above, as measured by RNA-seq and SOX2 (X-axis) expression.

Based on the RNASeq data generated for these cell lines, it was determined that all ORY-1001-sensitive and partially-sensitive cell lines express high levels of ASCL1 and moderate to high levels of SOX2, while in SCLC cell lines resistant to ORY-1001 treatment , very low levels of ASCL1 or SOX2 were detected (Log2(RPKM)≤0), as shown in Table 2 and Figure 1. Therefore, ASCL1 and SOX2 can be used as biomarkers to identify SCLC cells that are sensitive (i.e. responsive) or more likely to be sensitive (responsive) to treatment with KDM1A inhibitors such as ORY-1001 or have SCLC subjects.

Example 3 Validation of ASCL1 and SOX2 using qRT-PCR

The same set of SCLC cell lines described in Examples 1 and 2 (including two other SCLC cell lines, one identified as sensitive to KDM1Ai treatment (DMS53) and the other as sensitive to KDM1Ai treatment (DMS53) were then analyzed by Taqman qRT-PCR. The biomarkers responsive to KDM1A inhibitors identified in Example 2, ASCL1 and SOX2, were validated in KDM1Ai treatment partially sensitive (NCIH526)).

Gene expression analysis by qRT-PCR

Total RNA was extracted using the RNeasy Mini kit and cDNA was obtained using High Capacity RNA-to-cDNA Master Mix (ThermoFisher Scientific #4390779) according to standard procedures. qRT-PCR was performed using the LightCycler 480 Probes Master (PNT-L-034; Roche #04887301001) and using the pre-designed and pre-optimized TaqMan gene expression assay from ThermoFisher Scientific. qRT-PCR was performed in triplicate using Lightcycler 480 Instrument II (Roche; PNT-L-035). Cp values were analyzed by qRT-PCR in triplicate using the following Taqman primer/probe sets:

- ASCL1: Hs04187546_g1 (Life Technologies; amplicon length 81 bp, targeting exon 1-2 boundary, RefSeq NM_004316.3, see SEQ ID No. 1)

- SOX2: Hs01053049_s1 (Life Technologies; amplicon length 91 bp, targeting exon 1-1 boundary, RefSeq NM_003106.3, see SEQ ID No. 3)

- GAPDH: Hs02758991_g1 (Life Technologies; amplicon length 93bp, targeting exon 6-7 boundary, RefSeq NM_001256799.2)

result

ASCL1 and SOX2 gene expression in this panel of SCLC cell lines measured by qRT-PCR is shown in Table 3. The table reports Cp values. Exp.R: experimental replicate; Av.: average; n.d.: not detected. Each experimental replicate value is the average of three technical replicates. Each sample was analyzed for quantification of the same RNA by qRT-PCR. As a comparison, the average Cp expression of the GAPDH reference gene is reported, which ranged from 23 to 26 Cp in all samples (see Table 3).

table 3:

Table 4 shows the mean Cp values of all experimental replicates for ASCL1 and SOX2 in SCLC cell lines measured by qRT-PCR. The color codes displayed are based on gene expression levels; the darker the color, the higher the expression of the biomarker.

Table 4:

As shown in Tables 3 and 4, the expression of ASCL1 or SOX2 in KDM1Ai-resistant cells was not detected at all (Cp value >40), or had absolute Cp values higher than 35, indicating very low expression. On the other hand, all KDM1Ai-sensitive SCLC cell lines express ASCL1 and SOX2, thus confirming these findings using the RNASeq data described in Example 2. Among the partially sensitive cell lines, one was shown to express high levels of ASCL1 and SOX2, while the other had very low ASCL1 and SOX2 expression.

These results are also presented graphically in Figure 2, which is a dot plot representing SCLC cell lines sensitive, partially sensitive or resistant to KDM1A inhibition as described above, as measured by qRT-PCR (absolute Cp values) Gene expression of ASCL1 (Y axis) and SOX2 (X axis). The plotted values are the mean of independent experiments, as shown in Table 3. One of the cell lines exhibited SOX2 expression with a Cp value above 40; this is indicated by the dots shown in parentheses in Figure 2.

Thus, the results described herein further confirm that SCLCs sensitive to KDM1A inhibitor treatment generally exhibit high expression of ASCL1 and SOX2, whereas resistant SCLCs have low expression of either or both of ASCL1 and SOX2. Therefore, ASCL1 and SOX2 levels can be used as predictive biomarkers to identify SCLC cells or subjects with SCLC that have an increased likelihood of responding to KDM1A inhibitor treatment.

Example 4 Evaluation of predictive biomarkers for response to KDM1A inhibitors in an expanded panel of SCLC cell lines

To further validate the biomarkers ASCL1 and SOX2 as biomarkers responsive to KDM1A inhibition, a larger dataset was built and analyzed. SCLC viability assay data obtained using ORY-1001 were compared with publicly available Data on SCLC cell line responses to two additional KDM1A inhibitors (GSK2879552 and GSK-LSD1) were integrated and then used to classify a larger panel of SCLC cell lines as sensitive or resistant to KDM1A inhibitor treatment. An expanded set of this SCLC cell line is shown in Table 5 below. The chemical structures of GSK2879552 and GSK-LSD1 are provided in the specification.

table 5:

in:

*The cell line is sensitive to KDM1Ai, but requires more than 4 days of treatment to see an effect, as shown by the data for the other two KDM1A inhibitors tested over a longer period of time.

Certain cell lines are classified as partially sensitive because the sensitivity of these cell lines to KDM1A inhibition is assay dependent, with other assay conditions having obtained different results. The NCIH526 cell line was classified as partially sensitive because, although it was sensitive under the conditions tested here, it was reported to be resistant to ORY-1001 in other assays. NCIH2081 was classified as partially sensitive because some response to ORY-1001 was observed in vitro, but the maximal growth inhibition was very low (10%).

These KDM1Ai-sensitive and resistant cells were then analyzed using a cell line transcriptome dataset curated by the Broad Institute (Cancer Cell LineEncyclopedia; https://portals.broadinstitute.org/ccle; CCLE_Expression_Entrez_2012-09-29.gct.txt) The expression of ASCL1 and SOX2 was assessed in . Gene expression of ASCL1, SOX2 and GAPDH in SCLC cell lines as described in the CCLE database (Affymetrix microarray data; RMA values) are shown in Table 6 below. The p-value of the two-tailed Student's t-test was calculated using Microsoft Office Excel.

Table 6:

ASCL1 and SOX2 were differentially expressed between cell lines sensitive and resistant to KDM1Ai treatment (Table 6). Specifically, the mean expression of ASCL1 for sensitive and resistant cell lines was 12.25 and 7.19 normalized probe intensity units, respectively (p-value=3.0E-05). In the same cell lines, the mean normalized probe intensity values for SOX2 were 8.96 (sensitive group) and 5.75 (resistant group) (p value=2.0E-03). Expression of the GAPDH reference gene was similar in all samples (14.60 mean normalized probe intensity units for sensitive cell lines and 14.56 mean normalized probe intensity units for resistant cell lines, p-value=6.1E-01).

Consistent with the results described in Examples 2 and 3, 7 out of 8 KDM1Ai-sensitive SCLC cell lines highly expressed ASCL1 and SOX2, while almost all resistant SCLC cell lines expressed low levels of either ASCL1 and SOX2 or both. This is further highlighted in Figure 3, which shows dot plots representing ASCL1 and SOX2 in an expanded panel of SCLC cell lines sensitive, partially sensitive or resistant to KDM1A inhibition as measured by microarray Affymetrix analysis (RMA values) gene expression. As shown in Figure 3, a clear enrichment of cell lines sensitive to KDM1A inhibitors was observed in cell lines with high levels of ASCL1 and SOX2, and vice versa, in cell lines with low expression of either or both of ASCL1 and SOX2 Of the cell lines, there was a clear enrichment of cell lines resistant to KDM1A inhibitors.

Thus, the results described in Example 4 herein further support the use of these two biomarker combinations to identify/select subjects more likely to respond to treatment with a KDM1A inhibitor such as ORY-1001.

Gene expression values of sensitive and resistant cell lines for each of the selected biomarkers were analyzed using receiver operating characteristic curves (ROC curves) using GraphPad Prism 5.01 software ("partially sensitive" cell lines were excluded from the analysis). ROC curves were created by plotting the True Positive Rate (TPR) versus the False Positive Rate (FPR) at various threshold settings. Figure 4 shows ROC curves of ASCL1 (Figure 4A) and SOX2 (Figure 4B) expression to distinguish SCLC cells sensitive and resistant to KDM1A inhibitors. On the basis of the ROC curves for the respective biomarkers, the respective threshold levels for the best trade-off between selectivity and specificity (highest likelihood ratio) are reported. For the expression dataset in Table 6, using only the ASCL1 biomarker with a threshold level ≥8.935, KDM1A-sensitive and resistant cell lines could be distinguished with 68.42% sensitivity and 100% specificity. For the given expression dataset, using only the SOX2 biomarker with a threshold level ≥8.030, KDM1A-sensitive and resistant cell lines could be distinguished with 84.21% sensitivity and 87.50% specificity.

A different algorithm was then used to predict responses to KDM1A inhibition in the panel of SCLC cell lines described in Table 6 ("partially sensitive" cell lines were excluded from the analysis) based on the combination of ASCL1 and SOX2.

In the first example, a Boolean conjunctive model classification algorithm (Figure 4A and B) was constructed based on biomarkers that both met above the respective thresholds for ASCL1 and SOX2 above; if the algorithm met the criteria specified in the first row of Table 7 below condition, get a score of "1", otherwise get a score of "0". Cell lines were then classified as more likely to respond to KDM1Ai when the score exceeded a threshold, i.e. >0 (in this case, equal to 1) and less likely to respond when the score was equal to 0 (see table below 7). Finally, we evaluate the performance of the Boolean conjunction classification algorithm. Sensitivity (also known as True Positive Rate; TPR), Specificity (also known as True Negative Rate; TNR), Positive Predictive Value (PPV, also known as Precision), and Negative Predictive Value (NPV) were calculated, along with TPR and TNR, Geometric mean between PPV and NPV. Sensitivity was calculated as the ratio between true positives and the total number of positives. Similarly, specificity was obtained as the ratio between the number of true negatives and the total number of negatives. Positive predictive value (PPV; also called precision) and negative predictive value (NPV) were calculated using the following formulas:

PPV=TP/(TP+FP)

NPV=TN/(TN+FN)

where TP represents the number of true positives, TN represents the number of true negatives, FP represents the number of false positives and FN represents the number of false negatives.

Table 7:

The ASCL1/SOX2 marker had high sensitivity (88%), specificity (100%), precision (100%) and negative predictive value (95%).

Alternatively, support vector machine (SVM) modeling of the DTREG predictive modeling software is used to develop algorithms that can be used to classify samples using biomarkers. Specifically, the combination of ASCL1 and SOX2 was assessed as a predictive biomarker of responsiveness to KDM1A inhibitors using two different algorithms with different nuclei shown in Table 8 (top) functions, polynomials and radial basis functions (RBFs) and their respective parameters (C: cost parameter; γ: Kernell coefficient). Using the scores and thresholds (functions) determined by these algorithms, cell lines were classified as more likely to respond or less likely to respond to the indicated KDM1Ai, and the performance of the SVM model for classifying samples is shown at the bottom of Table 8, showing The generated SVM model was used to predict specificity, sensitivity and confusion matrices for sensitivity and resistance using the combination of ASCL1 and SOX2 expression.

Table 8:

Using this algorithm, using both polynomial and RBF fitting, the SVM algorithm with ASCL1/SOX2 combination has high sensitivity (88%) and high specificity (≥95%).

Alternatively, use linear regression of the DTREG predictive modeling software to develop algorithms for classifying samples as more likely to respond to KDM1Ai or less likely to respond to KDM1Ai as a function of the combination of biomarkers selected on the datasets in Table 6 ( Table 9). The algorithm calculates a score for each sample and classifies the cell line as sensitive or resistant to KDM1Ai by comparing the score of each sample to a threshold. The performance of the linear regression model generated based on the ASCL1/SOX2 biomarker combination had 87.5% sensitivity and 94.74% specificity for predicting sensitivity to KDM1Ai (Table 9).

Table 9 below shows the parameters, specificity, sensitivity, and confusion matrices of the resulting linear regression model for predicting sensitivity and resistance using a combination of ASCL1 and SOX2 expression. C: cost parameter; γ: kernel coefficient of the corresponding function.

Table 9:

Overall, this dual-marker designation was confirmed to be the best in predicting SCLC responsiveness to KDM1A inhibitors using different classification algorithms (Boolean conjunction model, SVM model, and linear model) that combined ASCL1 and SOX2 expression levels. performance.

Example 5 Correlation of ASCL1 and SOX2 mRNA with protein expression levels by Western blot (WB) and fluorescent immunohistochemistry (IF)

To test correlations between biomarker mRNA and protein levels, WB in SCLC cell lines expressing these biomarkers at high, moderate, or low/undetectable levels, as well as by SCLC cell pellets from the same sections and with Fluorescence immunohistochemistry of SCLC patient-derived xenografts (PDX) with known mRNA levels to analyze ASCL1 and SOX2.

5.1 Analysis of SOX2 and ASCL1 in SCLC cell lines by WB

Human small cell lung cancer (SCLC) cell lines NCI-H146, NCI-H510A, NCI-H446, NCI-H526 were grown at 37°C and 5% CO in a humidified atmosphere supplemented with ₂ mM glutamine and 10% FBS (Sigma ) in RPMI medium (Sigma). Cell pellets of 5 million exponentially growing cells were generated for whole protein extraction in RIPA buffer (Sigma) supplemented with protease inhibitors (Sigma) followed by ASCL1 determination by WB in 12% PAGE (Life Technologies). (Abcam, ab213151) and SOX2 (Abcam, ab97959) levels. Proteins were transferred using the iBlot System (Life Technologies), and after secondary antibody incubation and washing, blots were developed with ECL Prime (Amersham) and photographed with G:BOX Chemi XRQ (Syngene). Ponceau S staining of transfer blots was used as a loading control. WB signal was quantified using Image J. The integrated density of each WB band was normalized by the corresponding total protein integrated density of Ponceau staining and correlated with the NCI-H146 signal.

result

By WB of SCLC cell lines expressing these biomarkers at high, moderate or low/undetectable levels (as determined by qRT-PCR (Example 3), and with publications from Cancer Cell Lines Encyclopedia (CCLE) available As confirmed by the Affymetrix mRNA expression data (see Example 4)) ASCL1 and SOX2 protein levels were analyzed. The WB obtained is shown in Figure 5A, and the corresponding quantification of ASCL1 and SOX2 protein levels in NCI-H146, NCI-H510A, NCI-H446 and NCI-H526 cell lines is shown in Figure 5B. The protein expression levels of ASCL1 and SOX2 by WB were correlated with their corresponding mRNA levels, as ASCL1 was not detected in NCI-H446, ASCL1 and SOX2 were not detected in NCI-H526 cells, while their expression was in NCI-H146 The highest, the expression levels of both mRNAs and the specificity of the antibodies used were confirmed. Correlations between protein and mRNA (CCLE Affymetrix) levels of SOX2 and ASCL1 are plotted in Figures 6A and 6B, respectively; good correlations were observed with R values of 0.8957 for SOX2 and 0.9910 for ASCL1.

5.2 Analysis of SOX2 and ASCL1 by fluorescence immunohistochemistry on SCLC cell pellets

Ten million exponentially growing cells (NCI-H146, NCI-H510A, NCI-H446 and NCI-H526) were fixed in 10% formalin (Sigma) for 1 hr at room temperature and washed in 1XPBS (Sigma) , pelleted, contained in 1.3% agarose (Sigma), then dehydrated and contained in paraffin for microtome sectioning. 5 μm sections were placed on Superfrost slides, deparaffinized in HistoChoice detergent (Sigma) for 5 min, twice, and passed through a decreasing ethanol series (2x 100% 5 min, 90% 1 min, 70% 1 min, 30 % 1 min, 2X running water) to hydrate. Sections were then subjected to heat-induced antigen retrieval in boiling pH 6 citrate buffer IX (Sigma) for 20 minutes. After 20 minutes at room temperature, the slides were washed in PBS-Triton X100 0.1% (0.1% PBS-Tx) and blocked in 5% goat serum in 0.1% PBS-Tx for 1 hour at room temperature. After blocking, excess fluid was removed by capillary action with paper towels, and sections were incubated with primary antibodies (Abcam ab97959 at 1:500 dilution for SOX2; 1:500 dilution for ASCL1) in 1% goat serum in 0.1% PBS-Tx. 100 dilution of Abcam ab213151) and the corresponding negative control (only 1% goat serum in 0.1% PBS-Tx) were incubated overnight at 4°C. Each was washed for 5 min in 0.1% PBS-Tx, and after 3 washes, the slides were incubated with goat anti-rabbit AlexaFluor 546 secondary antibody (1:1500 dilution, Life Technologies A11010) for 1 hour at room temperature in the dark. Each was washed for 5 min in 0.1% PBS-Tx, after 5 washes, excess liquid was removed by capillary action with paper towels and the samples were encapsulated in Fluoroshield encapsulation medium supplemented with DAPI (Sigma). DAPI, 4',6-diamidino-2-phenylindole, is a fluorescent dye that binds strongly to A-T-rich regions of DNA and is used to stain cell nuclei. Nuclear signals from DAPI staining were used to identify and analyze the co-localization of nuclear-specific SOX2 and ASCL1 signals (see below). Images were taken in a Zeiss Axio fluorescence microscope with coupled AxioCam camera (Zeiss).

Quantification of IF intensity

IF images were processed and quantified using imageJ software. For quantification of SOX2 and ASCL1 nuclear-specific signals, a mask was created from the DAPI-stained images enclosing the area of nuclear coverage and then converted to the corresponding IF images to allow the determination of integrated densities in selected areas defined only by the nucleus.

result

To validate the antibodies used for fluorescent immunohistochemistry and to further confirm the correlation between ASCL1 and SOX2 protein and mRNA levels, the same antibodies used for WB were tested in fluorescent immunohistochemistry to examine the Levels of these biomarkers in SCLC cell pellets of embedded sections. Fluorescence immunohistochemistry for SOX2, ASCL1 and negative control is shown in Figure 7 (Figure 7A-SOX2, Figure 7B-ASCL1, Figure 7C-negative control with secondary antibody only (AF546)). Consistent with previously shown data, SOX2 levels were high in the NCI-H146 cell line, moderate in the NCI-H510A cell line, and low in the NCI-H446 cell line, while no nuclei were detected in the NCI-H526 cell line. specific expression. On the other hand, ASCL1 levels were high in NCI-H146, moderate in NCI-H510A, absent/undetectable in NCI-H446 and NCI-H526 cells. No expression was observed in the negative control (AF546: Alexa Fluor 546) using only the secondary antibody. Based on the staining intensity, the following staining intensity levels were established and used in the following examples from now on: high level will be defined as "level 3", medium level will be defined as "level 2", low level will be defined as "level 1" , no signal will be defined as "level 0".

Table 10 below shows the quantification of nuclear biomarker signals in the immunofluorescence images shown in Figure 7 and the corresponding intensity levels and RMA values from CCLE Affymetrix in NCI-H146, NCI-H510A, NCI-H446 and NCI-H526 cells. Signals were background corrected and expressed relative to the NCI-H146 signal (equivalent to 100%).

Quantification of nuclear signal for the cell lines analyzed revealed a highly statistically significant correlation between ASCL1 and SOX2 protein levels detected by fluorescent immunohistochemistry and corresponding mRNA levels (see Figures 8A-SOX2 and 8B- ASCL1). The corresponding R values for SOX2 and ASCL1 were 0.8710 and 0.9594, respectively.

Given the good correlations obtained between ASCL1 and SOX2 mRNA and protein levels as shown in Examples 5.1 and 5.2 above, confirming that measuring the mRNA or protein levels of ASCL1 and SOX2, ASCL1 and SOX2 can be used as markers in response to KDM1A inhibition Predictive biomarkers.

5.3 Fluorescence immunohistochemistry on patient-derived SCLC xenografts (PDX) tissue microarrays (TMA) Analysis of SOX2 and ASCL1

To further confirm the correlation between SOX2 and ASCL1 mRNA and protein levels in human samples, SOX2 and ASCL1 IF were performed on patient-derived SCLC xenograft tissue microarrays with available SOX2 and ASCL1 RNASeq data.

Tissue microarray sections containing 44 patient-derived SCLC xenografts were purchased from MolecularResponse (now Crown Bioscienses), and the corresponding RNASeq data available were downloaded from https://oncoexpress.crownbio.com/OncoExpress/index.aspx.

TMA was dewaxed in HistoChoice detergent (Sigma) twice for 5 min and hydrated by decreasing ethanol (2x 100% 5 min, 90% 1 min, 70% 1 min, 30% 1 min, 2X running water). Sections were then subjected to heat-induced antigen retrieval in boiling pH 6 citrate buffer IX (Sigma) for 20 minutes. After 20 minutes at room temperature, the slides were washed in PBS-Triton X100 0.1% (0.1% PBS-Tx) and blocked in 5% goat serum in 0.1% PBS-Tx for 1 hour at room temperature. After blocking, excess fluid was removed by capillary action with paper towels, and sections were incubated with primary antibodies (Abcam ab97959 at 1:500 dilution for SOX2; 1:500 dilution for ASCL1) in 1% goat serum in 0.1% PBS-Tx. 100 dilution of Abcam ab213151) and the corresponding negative control (only 1% goat serum in 0.1% PBS-Tx containing) were incubated overnight at 4°C. After 3 washes in 0.1% PBS-Tx for 5 min, slides were incubated with goat anti-rabbit Alexa Fluor 546 secondary antibody (1:1500 dilution, Life Technologies A11010) for 1 hour at room temperature in the dark. Each was washed for 5 min in 0.1% PBS-Tx, after 5 washes, excess liquid was removed by capillary action with paper towels and the samples were encapsulated in Fluoroshield encapsulation medium supplemented with DAPI (Sigma). Images were taken in a Zeiss Axio fluorescence microscope with coupled AxioCam camera (Zeiss).

Classification of PDX IF intensity

IF staining was analyzed with Zeiss Axio fluorescence microscopy and tumor areas were defined by nuclear morphology. The nuclear-specific signal intensity within the tumor region was visually divided into four levels, as described in Example 5.2:

Based on the intensities obtained in fluorescence immunohistochemistry performed on SCLC cell pellets and their corresponding known mRNA expression (Example 5.2), there will be expression levels above the respective biomarker threshold 1 for both ASCL1 and SOX2 (i.e. medium to high expression levels 2 and 3), or alternatively, samples with an ASCL1/SOX2 Boolean conjunctive score exceeding a threshold (>0) (ie, meeting both conditions for each biomarker) were classified as derived from more Patients likely to respond to KDM1Ai. In Figure 9, samples with scores above the threshold are denoted as "positive" and samples with scores not above the threshold are denoted as "negative".

Statistical Analysis

Two-tailed Spearman correlation tests between RNASeq (Log ₂ FPKM) and IF (visual scoring) datasets for SOX2 and ASCL1 were performed using GraphPad Prism software with 95% confidence intervals.

result

SOX2 and ASCL1 IF were performed on patient-derived SCLC xenograft tissue microarrays with available SOX2 and ASCL1 RNASeq data. All samples were visually analyzed under a fluorescence microscope for tumor areas defined by nuclear morphology, and intensity levels were given based on the nuclear signal observed within the tumor area, as described above. Representative ASCL1 and SOX2 staining from SCLC PDX TMA for each level of staining intensity classification is shown in FIG. 9 .

In two independent stainings of two consecutive SCLC PDX TMA sections (N=35 and N=43, respectively), IF visual scores and RNASeq data showed a highly statistically significant (P<0.0001) correlation with SOX2 The Spearman r values of ASCL1 were 0.7535 and 0.7659, respectively (Figures 10A and 10B), and the Spearman r values of ASCL1 were 0.8803 and 0.8989, respectively (Figures 10C and 10D).

The above results confirm that SOX2 and ASCL1 protein and mRNA levels are also correlated in patient-derived samples, thus by measuring ASCL1 and SOX2 mRNA or protein levels, ASCL1 and SOX2 mRNA or protein levels can be used in human samples as responses to KDM1A inhibition predictive biomarkers.

Example 6: ASCL1 and SOX2 can be detected in exosomes

Exosomes are microvesicles present in body fluids whose contents reflect the proteasome, genome and transcriptome of the parent cell. Thus, exosomes constitute an excellent minimally invasive tool for quantitative biomarker detection. Therefore, we tested whether our detection of ASCL1 and SOX2, predictive biomarkers of responsiveness to KDM1A inhibitors, is applicable to methods using exosome-containing samples.

Exosomes were isolated by precipitation from SCLC cell lines, and SOX2 and ASCL1 protein levels were determined by WB.

Detection of ASCL1 and SOX2 in exosomes

20 million NCI-H146, NCI-H510A, NCI-H446 and NCI-H526 SCLC cells were seeded in 20 ml RPMI medium supplemented with 2 mM glutamine (Sigma) and 10% exosome-free FBS (System Biosciences) (Sigma) and incubated in T75 flasks in a humidified atmosphere at 37°C and 5% _CO2 . After 48 hours, 15 ml of well-resuspended cells were spun at 2.000 x g for 30 minutes at room temperature, the supernatant was transferred to a clean tube, and the cell pellet was kept at -20°C. Exosome precipitation was performed using Total Exosome Isolation Reagent (from cell culture medium) (Life Technologies) using 10 ml of clarified medium exactly according to the manufacturer's instructions. The exosome pellet was then resuspended in 80 μl of IxSDS loading buffer for WB, or 40 μl of RIPA buffer (Sigma) supplemented with protease inhibitors for protein extraction. After quantification, 2 volumes of RIPA extract were mixed with 1 volume of 3xSDS loading buffer, heated to 95°C and kept at -20°C until used for WB.

Pass NCI-H510A cells grown under the conditions indicated above in the presence of 5 μM exosome release inhibitor GW4869 (SelleckChem) or vehicle, and using Total Exosome Isolation Reagent (from cell culture medium) exactly following the manufacturer's instructions ) (Life Technologies) to isolate exosomes and validate the exosome isolation method.

Cell pellets kept at -20°C were used for protein extraction in RIPA buffer (Sigma) supplemented with protease inhibitors. Following protein quantification using protein assay dye reagents (Bio-Rad), 7 μg of previously quantified antibodies were used for ASCL1 (Abcam, ab213151), SOX2 (Abcam, ab97959) and lung cancer exosome-specific CD151 marker (Abcam, ab33315) Total protein heated at 95°C in IX SDS loading buffer was used with 15 μl of exosomal protein extract for 12% PAGE by WB (Life Technologies). Blots were developed with ECL Prime (Amersham) and photographed with G:BOXChemi XRQ (Syngene). Ponceau S staining of transfer blots was used as a loading control.

result

To investigate the feasibility of ASCL1 and SOX2 assays in exosomes, microvesicle fractions from NCI-H146, NCI-H510A, NCI-H446 and NCI-H526 SCLC cells were isolated by precipitation using the method described in Example 5.1. Methods ASCL1 and SOX2 protein levels were analyzed by WB. The results obtained are shown in Figure 11, showing that both ASCL1 and SOX2 could be detected in exosomes and their expression was consistent with that in parental cells (see Figure 5). ASCL1 was not detected in neither NCI-H446 nor NCI-H526-derived exosomes (see Figure 11), but high levels of ASCL1 were detected in NCI-H146 and NCI-H510A cells. Conversely, SOX2 was not detected in NCI-H526-derived exosomes (Fig. 11), also consistent with the reported SOX2 expression in Fig. 5.

Furthermore, ASCL1, SOX2 and lung cancer-specific exosomal marker CD151 signaling were significantly reduced or abolished in the exosome fraction of NCI-H510A cells treated with 5 μM GW4869 (exosome release inhibitor) compared with vehicle, However, the expression of these proteins in the parental cells remained unchanged (Figure 12). Therefore, these results suggest that exosome-derived ASCL1 and SOX2 signals are specific to this microvesicle fraction obtained by precipitation and validate the exosome isolation method employed.

Overall, the results of this Example 6 demonstrate that, using exosomes as starting material/sample, the measurement of protein levels of the predictive biomarkers ASCL1 and SOX2 of the present invention can be performed to determine response to KDM1A inhibitors sex.

The present invention relates to the following nucleotide and amino acid sequences:

The sequences presented herein are available in the NCBI database and are available from www.ncbi.nlm.nih.gov/sites/entrez? db=gene search; these sequences also refer to annotated and modified sequences. The present invention also provides techniques and methods in which homologous sequences and variants of the condensed sequences provided herein are used. Preferably, such "variants" are genetic variants, such as splice variants.

Exemplary amino acid and nucleotide sequences of human ASCL1 and SOX2 are shown in SEQ ID NOs: 1 to 4 below. Exemplary nucleotide and amino acid sequences of human GAPDH (glyceraldehyde-3-phosphate dehydrogenase) used as a control gene in some embodiments are shown in SEQ ID NOs: 5 and 6.

SEQ ID No. 1: Nucleotide sequence encoding Homo sapiens Achaete-Scute family bHLH transcription factor 1 (ASCL1), mRNA

NCBI reference sequence: NM_004316.3. The coding region ranges from nucleotide 572 to nucleotide 1282 (highlighted in bold). It will be understood that the mRNA corresponds to (ie is identical to) the following sequence, except that the "t" (thymidine) residues are replaced by "uracil" (u) residues.

source

SEQ ID No.2: Amino acid sequence of Homo sapiens Achaete-Scute family bHLH transcription factor 1 (ASCL1), protein

UniProtKB/Swiss-Prot: ASCL1_person, P50553

SEQ ID No.3: Nucleotide sequence encoding Homo sapiens SRY-box2 (SOX2), mRNA

NCBI reference sequence: NM_003106.3. The coding region ranges from nucleotide 438 to nucleotide 1391 (highlighted in bold). It will be understood that the mRNA corresponds to (ie is identical to) the following sequence, except that the "t" (thymidine) residues are replaced by "uracil" (u) residues.

source

SEQ ID No.4: Amino acid sequence of Homo sapiens SRY-box 2 (SOX2), protein

UniProtKB/Swiss-Prot:SOX2_HUMAN,P48431

SEQ ID No. 5: Homo sapiens glyceraldehyde-3-phosphate dehydrogenase (GAPDH), mRNA

NCBI reference sequence: NM_002046.6. The coding region ranges from nucleotide 77 to nucleotide 1084 (highlighted in bold). It will be understood that the mRNA corresponds to (ie is identical to) the following sequence, except that the "t" (thymidine) residues are replaced by "uracil" (u) residues.

source

SEQ ID No. 6: Amino acid sequence of Homo sapiens glyceraldehyde-3-phosphate dehydrogenase (GAPDH), protein UniProtKB/Swiss-Prot: G3P_HUMAN, P04406

Claims (19)

WHAT IS CLAIMED IS: 1. A method of identifying SCLC patients more likely to respond to therapy comprising a KDM1A inhibitor, the method comprising measuring the levels of ASCL1 and SOX2 in a sample from the patient prior to initiating therapy comprising a KDM1A inhibitor. 2. The method of claim 1, further comprising identifying the patient as more likely to respond to therapy comprising a KDM1A inhibitor when the respective levels of ASCL1 and SOX2 in the sample exceed a threshold. 3. The method of claim 1, further comprising using the levels of ASCL1 and SOX2 in the sample to generate a score for the sample, wherein the patient is identified when the score in the sample exceeds a threshold for a more likely response to treatments containing KDM1A inhibitors. 4. A method of identifying SCLC patients who may benefit from treatment comprising a KDM1A inhibitor, the method comprising measuring the levels of ASCL1 and SOX2 in a sample from said patient prior to initiating said treatment comprising a KDM1A inhibitor. 5. The method of claim 4, further comprising identifying the patient as a patient likely to benefit from treatment comprising a KDM1A inhibitor when the level of each of ASCL1 and SOX2 in the sample exceeds a threshold. 6. The method of claim 4, further comprising using the levels of ASCL1 and SOX2 in a sample to generate a score for the sample, wherein the patient is identified as likely when the score in the sample exceeds a threshold Benefit from treatment containing a KDM1A inhibitor. 7. A method of selecting therapy for a patient with SCLC, the method comprising measuring the levels of ASCL1 and SOX2 in a sample from the patient prior to initiating therapy. 8. The method of any one of claims 1 to 7, wherein the ASCL1 level and SOX2 level are mRNA expression levels. 9. The method of claim 8, wherein the mRNA expression level is measured by qRT-PCR. 10. The method of any one of claims 1 to 7, wherein the ASCL1 level and SOX2 level are protein expression levels. 11. The method of claim 10, wherein the protein expression level is measured by fluorescence immunohistochemistry. 12. The method of any of claims 1 to 11, wherein the sample is a biopsy sample. 13. The method of any one of claims 1 to 12, further comprising recommending, prescribing to the patient if the patient is identified as more likely to respond to treatment comprising a KDM1A inhibitor Or administer a therapeutically effective amount of a treatment comprising a KDM1A inhibitor. 14. Use of a KDM1A inhibitor for the treatment of a patient with SCLC, wherein the patient has been identified as more likely to have a disease comprising a KDM1A inhibitor using the method according to any of claims 1 to 12 Therapeutic response to KDM1A inhibitors. 15. A method of treating a patient with SCLC, the method comprising if the patient has been identified as being more likely to have a disease if the method according to any of claims 1 to 12 is used prior to initiating treatment comprising a KDM1A inhibitor. response to the treatment comprising the KDM1A inhibitor, the patient is then administered a therapeutically effective amount of the treatment comprising the KDM1A inhibitor. 16. Use of ASCL1 and SOX2 in a method of identifying SCLC patients more likely to respond to therapy comprising a KDM1A inhibitor. 17. The method of claims 1 to 13, a KDM1A inhibitor for the use of claim 14, the method of treatment of claim 15 or the use of claim 16, wherein the The KDM1A inhibitor is (trans)-N1-((1R,2S)-2-phenylcyclopropyl)cyclohexane-1,4-diamine or a pharmaceutically acceptable salt thereof. 18. The method of claims 1 to 13 or 17, a KDM1A inhibitor for the use according to claim 14 or 17, the method of treatment according to claim 15 or 17 or according to claim 16 or 17 The use, wherein the patient is a human patient. 19. A kit for assessing the likelihood of a SCLC patient responding to a treatment comprising a KDM1A inhibitor, the kit comprising one or more reagents for measuring the levels of ASCL1 and SOX2 in a sample, and optionally ,user's manual.

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